# Causal Perception in Question-Answering Systems

Po-Ming Law  Georgia Institute of Technology<EMAIL_ADDRESS>,  Leo Yu-Ho Lo
The Hong Kong University of Science and Technology<EMAIL_ADDRESS>,  Alex
Endert  Georgia Institute of Technology<EMAIL_ADDRESS>,  John Stasko
Georgia Institute of Technology<EMAIL_ADDRESS>and  Huamin Qu  The Hong
Kong University of Science and Technology<EMAIL_ADDRESS>
(2021)

###### Abstract.

Root cause analysis is a common data analysis task. While question-answering
systems enable people to easily articulate a why question (e.g., why students
in Massachusetts have high ACT Math scores on average) and obtain an answer,
these systems often produce questionable causal claims. To investigate how
such claims might mislead users, we conducted two crowdsourced experiments to
study the impact of showing different information on user perceptions of a
question-answering system. We found that in a system that occasionally
provided unreasonable responses, showing a scatterplot increased the
plausibility of unreasonable causal claims. Also, simply warning participants
that correlation is not causation seemed to lead participants to accept
reasonable causal claims more cautiously. We observed a strong tendency among
participants to associate correlation with causation. Yet, the warning
appeared to reduce the tendency. Grounded in the findings, we propose ways to
reduce the illusion of causality when using question-answering systems.

correlation and causation, question answering

††journalyear: 2021††copyright: acmcopyright††conference: CHI Conference on
Human Factors in Computing Systems; May 8–13, 2021; Yokohama,
Japan††booktitle: CHI Conference on Human Factors in Computing Systems (CHI
’21), May 8–13, 2021, Yokohama, Japan††price: 15.00††doi:
10.1145/3411764.3445444††isbn: 978-1-4503-8096-6/21/05††ccs: Human-centered
computing Empirical studies in HCI

##  1\. Introduction

Root cause analysis is a common task during data analysis. Such analysis
provides explanations for events in business processes, observations about
human behaviours, and phenomena in society. A business analyst, for instance,
may seek explanations for a revenue decrease to identify supply chain
bottlenecks and marketing strategies (AI, 2017). To help people acquire this
important skill, colleges and online learning platforms have offered courses
on root cause analysis (Coursera, 2020; College, 2020).

The need for root cause analysis skills is not only limited to professional
analysts. Open data create opportunities for anyone to engage in personal data
projects. Visualization hobbyists, for example, may conduct data analysis on
public data and create fascinating visualizations on platforms such as
Makeover Monday (Monday, 2020). Individual citizens might analyze data about
the social issues they are concerned about and write a blog post about the
analysis (Lee, 2020; m00nlight Wang, 2018). However, root cause analysis could
be challenging to these people since they might lack domain knowledge and
analysis skills.

Systems with question-answering functionality present a resource that people
can utilize to explain data observations even without significant expertise in
data analysis. Users of these systems can easily articulate their why
questions through natural language (Dhamdhere et al., 2017) or point and click
(Tableau, 2020). The systems then employ advanced statistical analysis to
infer answers. Some technologists believe that question-answering interfaces
will become the norm in analytics platforms (Følstad and Brandtzæg, 2017).

However, causal inference from observational data (as opposed to randomized
experiments) is challenging (Rohrer, 2018). These question-answering systems
often produce unintuitive answers to a user’s why questions. Figure 1 shows
Explain Data, a question-answering functionality in Tableau (Tableau, 2020).
The user observes that Massachusetts has the highest average ACT Math score
among all US states. Being curious, she asks Explain Data to provide
explanations for the high score. Explain Data infers that the rate of teenage
pregnancy is negatively correlated with ACT Math score and that the low rate
of teenage pregnancy in Massachusetts may lead to the high ACT Math score. The
veracity of the explanation is questionable.

Figure 1. An answer generated by Tableau Explain Data (Tableau, 2020). The
user asks about the high ACT Math score in Massachusetts. Explain Data infers
that teenage pregnancy rate and ACT Math score are negatively correlated and
the low teenage pregnancy rate in Massachusetts might cause the high ACT Math
score. It shows the data using a scatterplot in which each dot is a state and
the blue dot is Massachusetts.

A screenshot of an explanation provided by Tableau Explain Data. It shows a
scatterplot with average ACT Math score on the X axis and average rate of
teenage pregnancy on the Y axis. Massachusetts is at the bottom right of the
point cloud, indicating that it has a high average ACT Math score and a low
average rate of teenage pregnancy. At the bottom of the scatterplot, there is
a textual description, saying that “This chart shows the correlation between
average ACT Math score and average rate of teenage pregnancy for all records
in the source visualization.”

When these systems do not always provide reliable results, a concern is their
potential power to persuade people into believing causal claims (e.g., low
teenage pregnancy rate in Massachusetts may lead to the high average ACT Math
score) that may not be true. Does visualizing correlation (e.g., using a
scatterplot) increase the plausibility of a causal claim and user trust in the
system even when the claim does not make sense? Does warning users about the
potential flaws in the system help them adopt the answers more cautiously?
Answering these questions could help understand designs that ensure judicious
use of the computational outputs.

This paper investigates the impacts of different information (a scatterplot, a
description about correlation, and a warning message) shown alongside a causal
claim on the perceptions of a question-answering system. We conducted two
crowdsourced studies with 200 participants each. In both studies, participants
reviewed a series of answers to why questions. These answers were presented
with different designs. Across different designs, we compared the perceived
plausibility of the causal claims, user trust in the question-answering
system, the awareness of the system’s flaws, and users’ tendency to associate
correlation with causation. Whereas the first study presented answers with
different degrees of plausibility, the second study presented only reasonable
answers.

From the first study, we found that participants tended to disagree less with
an unreasonable causal claim when a scatterplot was presented alongside the
claim. In contrast, participants appeared to accept a reasonable causal claim
more cautiously when they were shown a simple warning about the system’s
potential confusion of correlation and causation. We further observed a
general tendency among participants to associate correlation with causation,
but the warning seemed to reduce the tendency. We did not observe these
effects in the second study where the system only provided reasonable causal
claims.

Question-answering systems often employ data visualizations to provide context
for their answers (Hearst and Tory, 2019; Hearst et al., 2019). Our results
reveal that these systems could leverage the persuasive power of
visualizations to create an illusion of causality: Although scatterplots only
provide evidence about correlation, presenting scatterplots next to a causal
claim could increase users’ tendency to agree with the claim. Based on our
findings, we suggest that users should be skeptical when considering answers
that are automatically generated and propose design ideas to encourage
skepticism.

##  2\. Related Work

Our work intends to understand how the visual design of answers to why
questions might influence the perceptions of a question-answering system. We
draw on research relating to the impact of visualization design on data
interpretation as well as question-answering systems more broadly.

###  2.1. Impact of Visualization Design on Data Interpretation

Visualization design holds significant power to shape data interpretation
(Pandey et al., 2014). Researchers have investigated a wide range of factors
such as knowledge, perceptual biases, and cognitive biases that influence the
messages communicated to viewers.

Knowledge external to visualizations often affects how we interpret the
visualizations. As users look at a visualization, they often apply their
domain knowledge (Sacha et al., 2014). Xiong et al. (Xiong et al., 2019b)
showed that this prior knowledge could prime a viewer to obtain a particular
message from a visualization and lead the viewer to believe that other viewers
would receive the same message. Besides prior knowledge, social information
also affects data interpretation. Kim et al. (Kim et al., 2017) found that
seeing others’ expectations about the data influenced people’s trust in the
accuracy of the data.

Moreover, perceptual biases play a role in manipulating data interpretation
(Correll and Heer, 2017; Pandey et al., 2015). For example, distorting the
aspect ratio of a line chart can lead to an inaccurate assessment of trends in
the data (Heer and Agrawala, 2006); truncating the y-axis in a bar chart
exaggerates effect sizes (Correll et al., 2020); the neighborhood of a bar in
a bar chart can change the perceptions of the bar’s height (Zhao et al.,
2019). However, these biases could be mitigated through judicious design. For
instance, Ritchie et al. (Ritchie et al., 2019) showed that an animated
transition from an untruncated bar chart to a truncated one could avoid
misinterpretation.

Cognitive biases can further change the lens through which we interpret
visualizations (Dimara et al., 2020). An example is priming and anchoring
effects. Calero Valdez et al. (Valdez et al., 2017) conducted experiments to
show that the judgment of class separability in scatterplots depended on the
scatterplots users saw before. Biases in data interpretation can also have
consequences on decision making. Dimara et al. (Dimara et al., 2018) provided
evidence that the presence of dominated data points in a scatterplot
influenced the judgement of which points were dominating.

Besides knowledge and biases, subtle design choices also matter to data
interpretation: Titles can have a misleading impact on visualization
interpretation (Borkin et al., 2015; Hullman and Diakopoulos, 2011; Kong et
al., 2018, 2019); visual embellishments can affect the insights we gain from
visualizations (Boy et al., 2017; Moere et al., 2012).

While correlation does not imply causation, it is easy to confuse them,
leading to an illusion of causality (Matute et al., 2015). Xiong et al.’s
(Xiong et al., 2019a) found that this illusion would increase with the
aggregation level of data visualizations. Instead of studying data
aggregation, we investigated the effects of different information on perceived
causality when using question-answering systems. Specifically, we studied
whether two forms of correlational evidence (scatterplot and textual
description about correlation) could create causal illusion and whether a
simple warning could reduce the illusion.

###  2.2. Question-Answering Systems and User Perception

Technologists have developed question-answering systems to meet users’
information needs in various domains including sports (Zhi and Metoyer, 2020),
work settings (Liao et al., 2018), and data science (Fast et al., 2018). These
systems exhibit a wide variety of designs. Some (e.g., conversational agents
or chatbots) mimic natural human conversations and can understand a rich
diversity of topics (Apple, 2020). Others resemble web search and focus only
on a small set of tasks (Law et al., 2018, 2019).

In data visualization, researchers have developed natural language interfaces
to facilitate visual data analysis (Setlur et al., 2016; Srinivasan and
Stasko, 2017; Yu and Silva, 2019). Many of these systems aim to address
specific usability challenges as users employ natural language for data
analysis. For example, users’ utterances are often ambiguous. Datatone
utilizes ambiguity widgets to expose the ambiguity and let users correct the
system’s decisions (Gao et al., 2015). Moreover, conversations happen in some
context on which utterance semantics depend (Hoque et al., 2017). To address
this, Evizeon provides pragmatics support to retain contextual information and
infer a user’s meaning based on the context (Hoque et al., 2017).

Another line of research focuses on understanding the impact of system
behaviors and information presentation on the perceptions of these systems.
Liao et al. (Liao et al., 2016) investigated how agent sociability influences
user interactions with conversational agents. Ashktorab et al. (Ashktorab et
al., 2019) studied preferences for different strategies to handle
conversational breakdowns. Hearst et al. (Hearst and Tory, 2019; Hearst et
al., 2019) investigated the visual designs of answers provided by a natural
language interface and how users perceive these designs. In a similar vein, we
intend to provide insights into how the visual design of answers to why
questions might affect user perceptions of a question-answer system.

##  3\. Pre-Study: Collecting Causal Statements

As a starting point to understand the appropriate presentation of answers to
why questions, we focus on why questions about extremum (i.e., an extreme
value). An example is why students in Massachusetts have high ACT Math scores
on average (Fig. 1). Finding extremum is a common task during data analysis
(Amar et al., 2005). Also, functionality to answer such questions has emerged
in commercial systems such as Tableau (Tableau, 2020). Findings from our
studies could offer design guidelines in practice.

In study 1, we showed participants a series of answers to why questions. We
created answers with different visual designs and assessed user perceptions of
the system given the designs. Due to the inherent challenges in causal
inference (Rohrer, 2018), question-answering systems occasionally provide
unreasonable answers to why questions. To emulate these systems, we selected
causal claims with different levels of plausibility as answers presented to
participants. To select these causal claims, we conducted a pre-study.

###  3.1. Methods

####  3.1.1. Datasets

We planned to generate causal claims that were backed up by observational data
and considered using synthetic data. However, our goal was to study user
perceptions of a system, and the credibility of the data might affect user
perceptions. To control for the potential experimental confounds, we used
real-world data instead.

We first curated a dataset about states in the US from sources including US
Census Bureau (Bureau, 2020), National Center for Education Statistics (for
Education Statistics, 2020), and Kaiser Family Foundation (Foundation, 2020).
The curated dataset has 258 attributes about demographics, healthcare, and
education for each US state. We chose these topics because they are accessible
to laypeople. This enabled participants to judge the plausibility of the
generated causal claims based on common sense.

With the curated data table, we computed the Pearson correlation for all
attribute pairs. To find attribute pairs with a potential causal relationship,
we collected the ones with a high correlation (above 0.7 or below -0.7). For
each of the 1522 attribute pairs with a high correlation (e.g., employment
rate and poverty rate), we found a state (e.g., Mississippi) that has an
extreme value for both attributes and omitted the attribute pairs where such a
state did not exist.

Based on the collected attribute pairs and states, we generated causal claims
(e.g., low employment rate in Mississippi may be a factor that leads to the
high poverty rate in Mississippi). The plausibility of this claim can be
affected by the plausibility of the causal relationship (e.g., employment rate
affects poverty rate) and that of the information about the state (e.g.,
Mississippi has a high poverty rate). Since we intended to assess the
plausibility of the causal relationship, we removed the states from the causal
claims (see Fig. 2).

An author carefully picked 30 reasonable claims, 30 unreasonable claims and 30
claims that were hard to tell if they were reasonable (hereafter, hard-to-tell
claims). We verified and ranked the plausibility of these claims through a
study on Amazon Mechanical Turk (MTurk).

Figure 2. Interface used in the pre-study.

The interface participants interact with in the pre-study. It shows a causal
claim “Low employment rate may be a factor that leads to high poverty rate.”
Below the claim, there are three buttons with the labels “Reasonable,”
“Unreasonable,” “Not Sure.”

Figure 3. The top three reasonable, unreasonable, and hard-to-tell causal
claims. Each row shows a causal claim (right) and a bar chart that visualizes
the votes (left). The green, red, and gray bars represent the votes for
Reasonable, Unreasonable, and Not Sure respectively. For example, the most
reasonable claim was “a low employment rate may be a factor that leads to a
high poverty rate.” It got 20/20 votes for Reasonable (R), 0/20 vote for
Unreasonable (U), and 0/20 vote for Not Sure (N).

A ranked list of the top three reasonable claims, a ranked list of the top
three unreasonable claims, and a ranked list of the top three hard-to-tell
claims. The top three reasonable claims are “a low employment rate may be a
factor that leads to a high poverty rate,” “high access to exercise
opportunities may be a factor that leads to a high percentage of adults with
excellent health,” and “a high percentage of teens who abuse alcohol may be a
factor that leads to a high percentage of driving deaths with alcohol
involvement.” The top three unreasonable claims are “a low percentage of
adults who are obese may be a factor that leads to a high median housing
price,” “a high percentage of adults age 65+ with all teeth extracted may be a
factor that leads to a low percentage of households with internet access,” and
“low motor vehicle crash deaths may be a factor that leads to a high average
ACT Math score.” The top three hard-to-tell claims are “a high percentage of
adults who smoke may be a factor that leads to a high percentage of people
with vision difficulty,” “a high employment rate may be a factor that leads to
a low percentage of adults with poor physical health,” and “a low percentage
of infants who were breastfed may be a factor that leads to a low life
expectancy.”

####  3.1.2. Participants

We randomly segmented the 90 claims into five batches of 18 claims and
recruited 20 workers on MTurk to rate each batch (100 unique workers in
total). We limited the tasks to workers in the United States and had an
acceptance rate of 95% or above. During data analysis, we omitted participants
who failed to pass attention checks (but compensated them for participation).
We recruited participants until reaching the target sample size for each
batch. Participants were compensated $1 for the study that took approximately
5-10 minutes.

Among the 100 participants, 55 were male, and 45 were female. They aged 22-64
(M=35.5, SD=11.2). Participants reported their educational attainment to be
high school (8 participants), professional school (18), college (49), graduate
school (17), PhD (7), and postdoctoral (1).

####  3.1.3. Procedure

Each participant was randomly assigned to rate one of the five batches of 18
claims. Participants first filled out a demographic survey on their gender,
age, and highest education level. They then saw a series of 18 causal claims
that were presented on separate pages (Fig. 2). We randomized the presentation
order of these claims to prevent order effects. Based on the plausibility of
each claim, participants selected one of the three options: Reasonable,
Unreasonable, and Not Sure. As each participant rated more than a dozen causal
claims, we used the three options rather than a Likert scale with five options
or more to keep the study short. During the study, participants also answered
two attention check questions asking them to directly select one of the three
options.

###  3.2. Results

For each causal claim, we computed the probabilities that participants
selected Reasonable, Unreasonable, and Not Sure. We then calculated the
entropy for each claim. A low entropy implies that participants mostly voted
for the same option, whereas a high entropy means that participants’ votes
tended to distribute across the three options. Within each bucket of the 30
reasonable claims, 30 unreasonable claims, and 30 hard-to-tell claims, we
ranked the claims by entropy.

For the 30 reasonable claims, we ranked them in increasing order of entropy.
The top claims had a low entropy because participants mostly voted for
Reasonable. For the 30 unreasonable claims, we again ranked them in increasing
order of entropy. Participants mostly selected Unreasonable for the top
claims. For the 30 hard-to-tell claims, we expected that the claims where the
plausibility was the most difficult to judge had a high entropy score. This is
because participants likely struggled to choose among the options. We sorted
these claims in decreasing order of entropy.

Figure 3 shows the top three reasonable, unreasonable, and hard-to-tell claims
based on the above ranking. We provide a ranked list of all the 90 claims as a
supplementary material. Study 1 confirmed the validity of our results: In
study 1, when participants rated the claims on a 7-point Likert scale, they
tended to agree with the top reasonable claims, disagree with the top
unreasonable claims, and be neutral about the top hard-to-tell claims.

Figure 4. The four experimental conditions. A user asks about the high poverty
rate in Mississippi (a). The system answers only a causal claim (b), shows a
scatterplot next to the claim (c), adds a description about the correlation
(d), and warns about the system’s flaws besides showing the previous
information (e).

Screenshots of the four experimental conditions. The claim-only condition
shows only the causal claim “Low employment rate in Mississippi may be a
factor that leads to high poverty rate in Mississippi.” The claim + vis
condition shows a scatterplot with poverty rate on the X axis and employment
rate on the Y axis. In the plot, poverty rate and employment rate are
negatively correlated. The claim + vis + description condition shows the
description “As employment rate decreases, poverty rate tends to increase.”
next to the causal claim and the scatterplot. The claim + vis + description +
warning condition shows a message “The scatterplot only shows a correlation
between employment rate and poverty rate. Correlation, however, does not imply
causation. Interpret my answer with care!”

##  4\. Study 1: Providing Answers with Different Plausibility

With the ranked lists of reasonable, unreasonable, and hard-to-tell claims, we
designed a between-subject experiment during which participants reviewed a
series of answers to why questions.

When designing the presentation of answers, we considered its complexity to
typical end users. One way to answer why questions (e.g., why Mississippi has
a high poverty rate) is causal graph (Rohrer, 2018; Wang and Mueller, 2015), a
technique often employed in statistics literature for visualizing complex
causal relationships. However, causal graphs may require more advanced
statistics training to understand.

We also considered showing multiple factors in the answer but were concerned
about introducing experimental confounds. For example, the number, the
perceived plausibility, and the underlying causal relationship of the factors
could potentially alter the perception of system performance. Yet, using real-
world data implied that these variables could be difficult to control for.

We therefore adopted a simplified design where the system responded to a why
question by stating a factor that could answer the question. In each task,
participants saw a why question (e.g., why Mississippi has a high poverty
rate) and the system’s answer (e.g., low employment rate in Mississippi may be
a factor that leads to the high poverty rate). Across conditions, the answers
had different designs (Fig. 4b-d). We provide screenshots of the experiment
interface as a supplementary material.

Figure 5. The five stages in study 1 and study 2.

A flow chart showing the five stages in the experiment. In the figure, the
five stages are “collect demographic information,” “measure plausibility of
causal claims,” “measure user trust in system,” “assess awareness of reasoning
flaws in system,” and “assess tendency to associate correlation with
causation.”

###  4.1. Methods

####  4.1.1. Conditions

Building on prevailing system designs and the research literature, we focused
on two types of correlational evidence (scatterplot and textual description
about correlation) to investigate whether they created an illusion of
causality. We further studied the effectiveness of warning in reducing the
illusion. Here, we describe these three types of information:

Scatterplot. Scatterplots are common for showing the relationship between two
numerical variables (Sarikaya and Gleicher, 2017). They have also been applied
in question-answering functionality in commercial systems for showing the
relationship between cause and effect (Fig. 1).

Textual description about correlation. While the causal claim (e.g., low
employment rate in Mississippi may be a factor that leads to the high poverty
rate) describes a single state in the US, a description about correlation
(e.g., as employment rate decreases, poverty rate tends to increase) depicts
the overall trends for all the states. To facilitate interpretation,
visualization systems often provide such descriptions next to a chart (Law et
al., 2020; Srinivasan et al., 2018).

Warning message. Although scatterplots and the textual descriptions only
reveal correlation, they might induce an illusion of causality (Xiong et al.,
2019a). A mitigation strategy is to use a message to warn users that
correlation is not causation. While such warnings are less common in
visualization systems, they are commonly used in other systems (e.g., web
browser) to prompt safety-related behaviors (e.g., not to click on phishing
websites) (Egelman et al., 2008). It would be interesting to learn about if a
simple statement is enough to raise awareness of the system’s potential flaws
and reduce users’ tendency to confuse correlation and causation.

Based on these three information types, we designed four answer interfaces by
adding the information types one by one. Participants were randomly assigned
to a condition where the answers adopted one of the four designs:

Claim only (Fig. 4b). The system only shows a claim about cause and effect as
an answer to a why question.

Claim + vis (Fig. 4c). Beside the causal claim, the system visualizes the
cause and effect using a scatterplot. Prior studies showed that the aspect
ratio of point clouds in a scatterplot affects correlation estimation
(Cleveland et al., 1982; Micallef et al., 2017). To support a consistent
correlation estimation, we controlled the aspect ratio. For each axis, we set
the lowest value to be (min value of the data $-$ 0.15 $\times$ range of the
data) and the highest value to be (max $+$ 0.15 $\times$ range).

Claim + vis + description (Fig. 4d). The system additionally states the
correlation between the cause and effect variables with a textual description.

Claim + vis + description + warning (Fig. 4e). To encourage users to evaluate
the answers carefully, the system warns that the scatterplot only shows
correlation, and that correlation is not causation.

####  4.1.2. Participants

A power analysis indicated that for a significance level of 0.05 and a power
of 0.8, detecting a medium effect size of $f=0.25$ using one-way ANOVA
required 180 participants (45 participants per condition). As we planned to
conduct non-parametric tests (see Sec. 4.1.4), we targeted a slightly larger
sample size (200 participants in total or 50 participants per condition)
following guidelines on sample size determination for non-parametric tests
(Lehmann, 2006).

During participant recruitment, we limited the study to workers in the United
States, had an acceptance rate of 95% or above, and did not participate in the
pre-study. The study took approximately 10-20 minutes, and we compensated
participants $2.90. At the end, we recruited 200 unique workers on MTurk.

The survey had two interpretation checks for assessing scatterplot
comprehension and three open-ended questions (details in the Procedure
section). We excluded participants who did not pass any of the interpretation
checks or provided gibberish answers for any of the open-ended questions (but
compensated them for participation). Overall, the data quality was poor. For
example, many participants provided canned responses for some open-ended
questions. We omitted 123 participants and continued recruiting until reaching
the target sample size.

Participants aged 20-69 (M=35.4, SD=10.1). 131 were male, 68 were female, and
1 preferred not to say. They reported different educational attainments: high
school (29 participants), professional school (22), college (109), graduate
school (35), PhD (1), and postdoctoral (4). Concerning data analysis
expertise, 44 had none, 64 were beginners, 71 were intermediate, and 21 were
advanced. For experience with visualization platforms (e.g., Tableau), 82 had
none, 60 were beginners, 36 were intermediate, and 22 were advanced. When
asked about the frequency of using question-answering systems, 132 reported
never, 30 reported rarely, 23 reported weekly, and 15 reported daily.

Figure 6. Measuring tendency to associate correlation with causation.
Participants saw a data observation and a scatterplot (a), answered
interpretation check questions (b), and rated their agreement on a statement
suggesting that the scatterplot implied causation (c).

Screenshots of the survey section for assessing awareness of “correlation is
not causation.” First, participants saw a statement “Mississippi has the
second highest child mortality among all US states.” Below the statement is a
scatterplot with child mortality on the X axis and Variable X on the Y axis.
The plot shows a positive correlation between the two variables. From the
plot, Mississippi has a high value in both child mortality and Variable X.
Based on the scatterplots, participants were asked two interpretation check
questions. The first question says, “Which of the following statements about
the scatterplot is true?” There are two options: “As Variable X increases,
child mortality tends to decrease,” and “As Variable X increases, child
mortality tends to increase.” The second question says, “Based on the
scatterplot, which of the following statements about Mississippi is true?”
There are two options: “Mississippi has a lower value of Variable X than most
other US states,” and “Mississippi has a higher value of Variable X than most
other US states.” After the interpretation checks, participants were asked,
“To which extent do you agree or disagree with this statement: ‘The
scatterplot implies that the high value of Variable X is a factor that leads
to the high child mortality in Mississippi.”’ Below this question, they could
answer by selecting a value from -3 (meaning strongly disagree) to +3 (meaning
strongly agree).

####  4.1.3. Procedure

We first randomly assigned participants to one of the four conditions. For all
conditions, the study consisted of five main stages (Fig. 5).

In stage 1 (Fig. 5 1), participants filled out a demographic survey. After
filling out the survey, they completed a practice task to get acquainted with
the study interface.

In stage 2 (Fig. 5 2), participants reviewed a series of nine answers to why
questions. In each task, they examined a data observation (e.g., Mississippi
has the highest poverty rate among all US states) (Fig. 4a), a why question
(e.g., why is poverty rate in Mississippi so high?) (Fig. 4a), and the
system’s answer to the question (Fig. 4b-d). Depending on the condition,
participants saw a different visual design for the answers. Based on the
system’s answer, participants rated their agreement with a causal claim (e.g.,
low employment rate in Mississippi is a factor that leads to high poverty rate
in Mississippi) on a 7-point Likert scale.

We constructed the nine answers using the top nine causal claims obtained from
the pre-study (Fig. 3). Hence, three answers were reasonable, three were
unreasonable, and three had plausibility that was difficult to judge. This
intended to mirror real-world systems that tend to be unreliable in answering
why questions. The order of the answers was randomized to prevent order
effects.

After participants reviewed the nine answers, we measured user trust in the
system in stage 3 (Fig. 5 3). Participants rated their trust in the system on
a 7-point scale from -3 (I don’t trust it at all) to +3 (I fully trust it).
They further shared their reasons for trusting or not trusting the system.

Next, we assessed their awareness of the reasoning flaws in the system in
stage 4 (Fig. 5 4). Participants reported whether they observed any reasoning
flaws in the system. If the answer was “yes,” we asked them to specify the
reasoning flaw(s) they found.

In the final stage (Fig. 5 5), we assessed their tendency to associate
correlation with causation. Participants in stage 5 saw a description of a
data observation (Mississippi has the second highest child mortality among all
US states) and a scatterplot showing a strong correlation between child
mortality and an unknown variable X (Fig. 6a). To assess participants’
understanding of scatterplots, we first asked participants to answer two
interpretation check questions (Fig. 6b). Participants who failed to pass any
of the questions were excluded from the data analysis.

Whereas participants rated their agreement with a causal relationship in stage
1, participants rated their agreement with a sentence stating that a
scatterplot with a high correlation implied a causal relationship in stage 5.
Participants saw a statement: “The scatterplot implies that the high value of
Variable X is a factor that leads to the high child mortality in Mississippi”
(Fig. 6c). They rated the statement on a 7-point Likert scale and explained
why they agreed or disagreed.

####  4.1.4. Quantitative Measures

We derived six measures from participants’ response.

Agreement (reasonable). For each participant, we computed the average
agreement rating for the three reasonable answers.

Agreement (unreasonable). It is the average rating for the three unreasonable
answers.

Agreement (hard to tell). It is the average rating for the three answers that
were hard to tell if they were reasonable.

Trust. Some researchers have developed questionnaires to assess user trust in
recommender systems (Pu et al., 2011) and machine learning systems (Cheng et
al., 2019). Since these questionnaires may not be applicable to question-
answering systems, we tailored a question to assess trust in question-
answering systems. In the post-study survey, we asked, “Overall, how much do
you trust or not trust the question-answering system?” and participants rated
on a scale from -3 to +3.

Awareness of system’s flaws. We computed the number of participants who
selected “yes” for the question, “Did you observe any flaw(s) in the reasoning
of the question-answering system?” Unlike the other measures that are scales
between -3 and +3, this measure is a count between zero and 50. Whereas trust
and agreement with answers are more subjective, observations about reasoning
flaws in the system are more clear-cut, making a yes/no question more
suitable.

Awareness of “correlation is not causation”. In the last part, participants
rated a statement: “The scatterplot implies that the high value of Variable X
is a factor that leads to the high child mortality in Mississippi.” (Fig. 6c)
If participants were cautious about drawing causal conclusions from
correlation, they should be inclined to disagree with the statement.

During a pilot study, we observed that when the variable name was shown,
participants tended to use their common sense to decide if they agreed with
the statement. Yet, we wanted to assess tendency to confuse correlation and
causation instead of ability to apply common sense. To reduce the impact of
common sense in answering the question, we hid the variable name of X.

Likert-scale data are not continuous and violate the ANOVA assumptions. To
study the main effect of answer design, we used a Kruskal-Wallis test, which
is a non-parametric equivalence of one-way ANOVA, for the five measures using
a 7-point scale (i.e., all measures except awareness of system’s flaws). When
there is a significant main effect, we conducted post-hoc Wilcoxon rank sum
tests with a Holm-Bonferroni correction for pairwise comparisons.

For the awareness of system’s flaws, we used a Fisher’s exact test to assess
if the number of participants who found reasoning flaws in the system was
significantly different across conditions.

Figure 7. Quantitative results from study 1. All error bars show 95%
bootstrapped confidence intervals.

Six charts showing the quantitative results for the six quantitative measures
in study 1. Each chart shows the measures for the four conditions. For
example, in the chart for the measure “awareness of system’s flaws,” there are
28, 25, 25, and 19 participants who observed reasoning flaws in the system for
the claim-only, claim + vis, claim + vis + description, and claim + vis +
description + warning conditions respectively. Other numbers can be found in
the text.

####  4.1.5. Qualitative Response

There were three open-ended questions in the survey, one for explaining trust
or distrust, one for specifying reasoning flaws in the system, and one for
explaining why agreed or disagreed that the scatterplot implied causation.

For each question, an author open-coded the responses to identify the emergent
categories and develop a codebook. We observed that a response could include
multiple categories. Hence, we treated each category as binary: For each
response, we labelled whether each category was present or absent. Two coders
independently coded all responses. We then discussed inconsistencies, refined
code definitions, and independently re-coded the responses based on the new
definitions. We iteratively coded the responses until we reached a Cohen’s
$\kappa$ above 0.7 for all the categories.

For each category, we conducted a Fisher’s exact test to determine whether its
presence was significantly different across conditions.

####  4.1.6. Hypotheses

We developed hypotheses based on research in visualization’s persuasive power,
trust in automated systems, and warning science.

Pandey et al. (Pandey et al., 2014) found that when participants did not have
a strong attitude towards a topic, visualizations had a strong power to change
their attitudes. They also commented on the difficulty to change attitudes for
topics of which participants already had a strong prior opinion (Pandey et
al., 2014). We expected that showing a scatterplot would increase the
plausibility of hard-to-tell claims because participants likely did not have a
strong attitude towards them. We also expected that the scatterplot would not
affect the plausibility of reasonable and unreasonable claims.

H1.1: Participants’ agreement with the reasonable claims does not differ
across conditions.

H1.2: Participants’ agreement with the unreasonable claims does not differ
across conditions.

H1.3: Participants in the three conditions that show a scatterplot in the
answers (i.e., claim + vis, claim + vis + description, and claim + vis +
description + warning) agree with the hard-to-tell claims more than
participants in the claim-only condition.

Transparency in automated systems can inspire user trust (Sinha and
Swearingen, 2002). For example, when a recommender system provides reasons
behind its recommendations, users tend to trust the system more (Herlocker et
al., 2000). Showing the data can increase the transparency in the question-
answering system. We posited that users would trust the system more when it
showed the scatterplot.

H1.4: Participants in the three conditions that show a scatterplot in the
answers trust the question-answering system more than participants in the
claim-only condition.

Some researchers in warning science have compared the effectiveness of passive
and active warnings (Egelman et al., 2008). Whereas active warning forces
users to notice it by blocking user tasks, passive warning (e.g., a simple
warning message) is less interrupting (Egelman et al., 2008). In data
analysis, passive warning is more suitable because a small latency in
interaction can hamper analysis quality (Liu and Heer, 2014). However, Egelman
et al. (Egelman et al., 2008) showed that passive warnings were often
ineffective because users might ignore them. The ineffectiveness might extend
to question-answering systems. Hence, we posited that the warning message
would not increase participants’ awareness of the system’s flaws nor decrease
their tendency to associate correlation with causation.

H1.5: Participants’ awareness of the system’s flaws does not differ across
conditions.

H1.6: Participants’ awareness of “correlation is not causation” does not
differ across conditions.

###  4.2. Results

Figure 7 summarizes the results for the quantitative measures. We observed
that the scatterplot increased the plausibility of unreasonable and hard-to-
tell claims but not reasonable claims. The warning message appeared to
decrease the plausibility of reasonable claims but not unreasonable and hard-
to-tell claims. Trust and awareness of flaws did not seem to differ across
conditions. However, the warning message seemed to increase the awareness of
“correlation is not causation.”

Also, for the claim-only condition, participants tended to give a neutral
rating for the hard-to-tell claims (M=0.04), a positive rating for the
reasonable claims (M=1.95), and a negative rating for the unreasonable claims
(M=-1.44). This confirmed the validity of the pre-study results.

In the following, we provide the detailed analysis.

####  4.2.1. Agreement (reasonable)

On a scale from -3 (strongly disagree) to +3 (strongly agree), participants in
the claim + vis + description condition rated the reasonable claims the
highest (M=2.13, SD=0.92), followed by those in the claim + vis condition
(M=2.04, SD=0.98), the claim-only condition (M=1.95, SD=0.96), and the claim +
vis + description + warning condition (M=1.65, SD=0.89). A Kruskal-Wallis test
indicated a significant main effect of answer design on the rating
($\chi^{2}(3)$=10.8, p=.013). We conducted six post-hoc pairwise comparisons
using Wilcoxon rank sum tests with a Holm-Bonferroni correction. Results
showed that only the difference between claim + vis + description + warning
and claim + vis + description (p=.017) as well as that between claim + vis +
description + warning and claim + vis (p=.044) were significant. The results
did not support H1.1.

####  4.2.2. Agreement (unreasonable)

Participants in the claim + vis + description condition rated the unreasonable
claims the highest (M=-0.26, SD=1.92), followed by those in the claim + vis
condition (M=-0.47, SD=1.96), the claim + vis + description + warning
condition (M=-0.55, SD=1.82), and finally the claim-only condition (M=-1.44,
SD=1.74). A Kruskal-Wallis test indicated a significant main effect of answer
design on the rating ($\chi^{2}(3)$=12.2, p=.007), with post-hoc pairwise
comparisons showing that all the three conditions with scatterplots in the
answers had a significantly higher average rating than the claim-only
condition. The results did not support H1.2.

####  4.2.3. Agreement (hard to tell)

Participants in the claim + vis + description condition rated the hard-to-tell
claims the highest (M=1.2, SD=1.12), followed by those in the claim + vis
condition (M=0.87, SD=1.34), the claim + vis + description + warning condition
(M=0.62, SD=1.25), and the claim-only condition (M=0.04, SD=1.05). There is a
significant main effect of answer design on the rating ($\chi^{2}(3)$=24.4,
p¡.001). Pairwise comparisons showed that all the three conditions with
scatterplots in the answers had a significantly higher average rating than the
claim-only condition. Other pairs were not significantly different. The
findings supported H1.3.

####  4.2.4. Trust

On average, the trust ratings across conditions were positive, indicating a
tendency to trust the system. Claim + vis + description + warning has the
highest rating (M=0.98, SD=1.31), followed by claim + vis + description
(M=0.9, SD=1.76), claim + vis (M=0.76, SD=1.67), and claim-only (M=0.18,
SD=1.70). However, we did not observe a significant main effect of answer
design on trust ($\chi^{2}(3)$=7.34, p=.062). The results did not support
H1.4.

####  4.2.5. Why trust or not trust?

We coded participants’ reasons for trusting or not trusting the question-
answering system. Seven categories of responses emerged from the analysis. We
report the core results here and provide the detailed breakdown of the
categories across conditions in the supplementary materials.

For each response, we labelled each category as present or absent. We labelled
all categories as absent for responses that were too broad or vague (e.g., “it
is nice”).

The top three reasons for distrusting the system were some answers did not
make sense (40.5% of 200), the system confused correlation and causation (9%),
and it did not provide enough support for its causal claims (7.5%). A
participant felt that some claims lacked support and wrote, “Some of the
answers could be factual but it was hard to determine without further data.”

The top three reasons for trusting the system were that some answers made
sense (26%), the system showed the data (8.5%), and the system provided some
support for its causal claims (6.5%)

We did not observe a significant difference in the presence of any of the
categories across conditions using Fisher’s exact tests (details in
supplementary materials).

####  4.2.6. Awareness of system’s flaws

Using a Fisher’s exact test, we did not find a significant difference in the
number of people who found reasoning flaws (these participants selected “yes”
for the question asking whether they observed reasoning flaws) across
conditions (p=.33). We could not reject H1.5.

####  4.2.7. What are the reasoning flaws?

The qualitative coding resulted in four categories. Among the 97 participants
who observed reasoning flaws in the system, the majority of participants
stated providing nonsensical answers as a reasoning flaw (70.1% of 97). Other
observed reasoning flaws were confusing correlation and causation (15.5%), not
having enough support for the claims (8.25%), and considering only one factor
(3.09%). Fisher’s exact tests did not indicate significant differences in the
presence of any of the four categories across conditions.

####  4.2.8. Awareness of “correlation is not causation”

We asked participants to rate a sentence stating that a scatterplot implied
causation. On a scale from -3 (strongly disagree) to +3 (strongly agree),
claim + vis + description + warning had the lowest rating (M=1.02, SD=1.45),
followed by claim + vis + description (M=1.6, SD=1.59), claim-only (M=1.82,
SD=1.10), and claim + vis (M=1.88, SD=1.33). All conditions got a positive
average rating, indicating a tendency to associate correlation with causation.
We found a significant main effect of answer design on the rating
($\chi^{2}(3)$=15.2, p=.002). Post-hoc pairwise comparisons showed that claim
+ vis + description + warning had a significantly lower average rating than
all the other three conditions, indicating that the warning appeared to reduce
the tendency to associate correlation with causation. The results did not
support H1.6.

####  4.2.9. Why agree or disagree with the statement?

The qualitative coding yielded four categories. We again observed that some
responses were overly broad (e.g., “because the graph shows it”) and coded all
categories as absent for such responses.

Among the more specific responses, the majority of participants agreed that
the scatterplot implied a causal relationship because the scatterplot showed a
correlation (46% of 200). An example response is “If Variable X did not rise
then child mortality would not rise.”

Participants disagreed with the statement because correlation is not causation
(8.5%), variable X was unknown and they could not judge (8.5%), and the
scatterplot had outliers (4%). A participant who observed outliers said, “I
only slightly agree because other states show otherwise. Texas, for instance,
has a much lower Child Mortality rate but Variable X is almost the same.”

We did not find significant differences in the presence of any of the
categories across conditions.

Figure 8. Quantitative results from study 2. All error bars show 95%
bootstrapped confidence intervals.

Four charts showing the quantitative results for the four quantitative
measures in study 2. Each chart shows the measures for the four conditions.
For example, in the chart for the measure “awareness of system’s flaws,” there
are 7, 14, 7, and 7 participants who observed reasoning flaws in the system
for the claim-only, claim + vis, claim + vis + description, and claim + vis +
description + warning conditions respectively. The three other charts are
agreement with reasonable claims, trust, and awareness of “correlation is not
causation.” These three charts do not show statistically significant
differences across the four conditions.

##  5\. Study 2: Providing Only Reasonable Answers

Several findings from study 1 deviated from our expectations: The simple
warning appeared to decrease the plausibility of reasonable claims and
increase the awareness of “correlation is not causation”; we did not have
enough evidence that user trust was improved by showing the data. A potential
explanation lied in the unreliable performance of the system—it made the
warning more noticeable and reduced the effectiveness of showing the data in
improving user trust (when the system performed poorly, it was untrustworthy
no matter whether it showed the data). To investigate whether the observations
in study 1 held for a system that had a higher perceived performance, we
conducted study 2.

###  5.1. Methods

Study 2 was the same as the study 1 except that participants reviewed nine
reasonable answers to why questions (as opposed to reviewing answers with
different levels of plausibility in study 1). We constructed the answers using
the top nine claims in the ranked list of 30 reasonable claims obtained from
the pre-study.

We similarly recruited 50 participants per condition (200 unique workers in
total). Workers who participated in the pre-study and study 1 were excluded
from study 2. Participants aged 18-70 (M=36.1, SD=11.0). 121 were male, 77
were female, and 2 preferred not to say. The reported educational attainments
were high school (28 participants), professional school (10), college (116),
graduate school (37), PhD (8), and postdoctoral (1). Concerning data analysis
expertise, 44 had none, 79 were beginners, 54 were intermediate, and 23 were
advanced. For experience with visualization platforms (e.g., Tableau), 84 had
none, 47 were beginners, 45 were intermediate, and 24 were advanced. When
asked about the frequency of using question-answering systems, 123 reported
never, 35 reported rarely, 31 reported weekly, and 11 reported daily.

As the system only presented reasonable answers, study 2 only had four
measures: agreement (reasonable), trust, awareness of system’s flaws, and
awareness of “correlation is not causation.”

In study 1, participants heeded the warning, causing them to agree less with
reasonable claims and be less likely to associate correlation with causation.
We expected that both effects would disappear when the system was more
trustworthy. Furthermore, in study 1, showing the data using a scatterplot did
not seem to improve user trust in the system. We posited that when the system
provided only reasonable answers, showing the data would improve user trust.
We considered the same set of hypotheses as in study 1:

H2.1: Participants’ agreement with the reasonable claims does not differ
across conditions.

H2.2: Participants in the three conditions that show a scatterplot in the
answers trust the question-answering system more than participants in the
claim-only condition.

H2.3: Participants’ awareness of the system’s flaws does not differ across
conditions.

H2.4: Participants’ awareness of “correlation is not causation” does not
differ across conditions.

###  5.2. Results

Figure 8 shows the quantitative results. Kruskal-Wallis tests for agreement
(reasonable), trust, and awareness of “correlation is not causation” as well
as a Fisher’s exact test for awareness of system’s flaws indicated no
significant differences across conditions (details in the supplementary
materials). Hence, the results failed to support H2.2. However, we could not
reject H2.1, H2.3, and H2.4.

We also observed that participants in study 2 appeared to trust the system
more than those in study 1. The mean trust rating in study 2 was 1.70
(SD=1.04) while that in study 1 was 0.71 (SD=1.64). Participants in study 2
also found fewer reasoning flaws in the system. The total number of
participants who found reasoning flaws in study 2 was 35 (compared with 97 in
study 1). We summarize the qualitative results as follows.

####  5.2.1. Why trust or not trust?

Participants provided diverse reasons for trusting or not trusting the system.
Seven categories of reasons emerged from the qualitative coding.

The top three reasons for trusting the system were the answers made sense
(38.5% of 200), the system provided enough support for its causal claims
(19.5%), and it showed the data (8%). The top three reasons for distrusting
the system were the system did not provide enough support for its claims (8%),
it considered only one factor (7%), and it confused correlation and causation
(2.5%).

Fisher’s exact tests indicated that the number of participants stating “the
answers made sense” as a reason was significantly different across conditions
(p=.014). We conducted six post-hoc pairwise comparisons using Fisher’s exact
tests with a Holm-Bonferroni correction. We only observed that more
participants in the claim-only condition stated “the answers made sense” than
in the claim + vis + description condition (p=.025). A potential explanation
was that providing a claim only led participants to comment mostly on the
plausibility of the claim. However, providing other information (e.g., a
scatterplot) alongside a claim enabled them to comment on other aspects and
less on the plausibility.

In study 2, 56% of the responses contained reasons for trusting the system
while 16% contained reasons for not trusting it. The data stood in contrast to
those in study 1. In study 1, 39.5% of the responses had reasons for trust
while 49.5% had reasons for distrust. This echoed the finding that
participants trusted the system more in study 2.

####  5.2.2. What are the reasoning flaws?

Among the 35 participants who answered “yes” for the question asking whether
they observed reasoning flaws in the system, we found three categories of
responses after omitting those who provided vague answers: The system
considered only one factor (28.6% of 35); it confused correlation and
causation (20%); it did not provide enough support for the claims (17.1%).
Using Fisher’s exact tests, we did not observe significant differences in the
presence of the categories across conditions.

####  5.2.3. Why agree or disagree with the statement?

The qualitative analysis resulted in five categories of responses. Congruent
with study 1’s results, most participants agreed that the scatterplot implied
a causal relationship because the scatterplot showed a correlation (43.5% of
200).

Participants who disagreed with the statement commented that correlation is
not causation (7.5%), the scatterplot had outliers (7%), variable X was
unknown and they could not judge (5.5%), and the dots in the scatterplot
looked disperse (1.5%).

Fisher’s exact tests did not show a significant difference across conditions
for any of the categories.

##  6\. Discussion

Before discussing the implications of our findings, we summarize the results
from the two studies and provide potential explanations for the less intuitive
observations.

In study 1, participants reviewed answers of different plausibility. We did
not observe effects of the textual description about correlation on the
perceived plausibility of causal claims, user trust in the system, the
awareness of the system’s flaws, and the awareness of “correlation is not
causation.” However, showing a scatterplot caused participants to disagree
less with unreasonable claims and agree more with hard-to-tell claims. In
contrast, a simple warning message seemed to cause participants to agree less
with reasonable claims. The warning also reduced participants’ tendency to
associate correlation with causation.

Nevertheless, when participants examined only reasonable answers in study 2,
the impact of the simple warning message on reducing the plausibility of
reasonable claims and on raising the awareness of “correlation is not
causation” seemed to disappear. Research in warning science found that arousal
strength (i.e., the perceived importance or relevance of a warning) affects
the effectiveness of a warning message in motivating safety-related behaviors
(Hellier et al., 2000). Participants in study 2 tended to trust the system
more than those in study 1. This likely led participants in study 2 to
perceive the warning about the system’s reasoning flaws to be less relevant.
The warning in study 2 became less effective possibly because participants
tended to ignore the warning.

In both studies, we did not observe significant differences in user trust and
the awareness of the system’s flaws across conditions. The qualitative results
provided an explanation. In study 1, when asked about why they did not trust
the system or what were the reasoning flaws in the system, most participants
simply stated that the answers did not make sense. In study 2, when asked
about why they trusted the system, the majority commented that the answers
made sense to them. The results appeared to indicate that system performance
in answering why questions had a dominating effect on user trust and the
awareness of reasoning flaws in the system. In other words, when users can
assess system performance, showing other information (e.g., a scatterplot or a
warning) may play a small role in shaping user trust and the awareness of
flaws.

We observed a tendency for participants to conclude causation from
correlation. In both studies, we found that the ratings for the awareness of
“correlation is not causation” were positive (i.e., agreeing that the
scatterplot implied causation) even when participants were warned that
correlation is not causation. How do we reduce the illusion of causality when
using question-answering systems? Here, we devise design considerations based
on the study results.

###  6.1. Encouraging Skepticism When Using Question-Answering Systems

A core implication of our results is that question-answering systems could
utilize visualizations of correlation to create an illusion of causality: By
showing a scatterplot, these systems could increase the likelihood for users
to accept causal claims that are unfounded. Mitigating the illusion of
causality entails a deliberate design effort. In this section, we argue that
encouraging users to be skeptical about automated answers could promote an
appropriate interpretation of automatically generated causal claims and
propose design ideas to inspire skepticism.

Why should users be skeptical when considering causal claims that are
automatically generated? In a perfect world, user trust in a system’s answer
should match the ground truth—users should trust causal claims only when they
are true and distrust false claims (Fig. 9a). In reality, however, belief in
causal claims depends on their perceived plausibility despite the ground truth
(Fig. 9b). Very often, users can only determine the plausibility of a causal
claim but not whether it is true. Hence, we advocate that users should be
skeptical whenever they cannot assess the veracity of a causal claim (Fig.
9c): A good data consumer should question the validity of a reasonable claim
because the causal relationship could be fake; she should not refute the
possibility of an unreasonable claim since the claim could hold true.

Figure 9. The relationship among trust, a claim’s plausibility and the ground
truth in different scenarios. In each bigger square, the y-axis is a claim’s
plausibility and the x-axis is the ground truth. In a perfect world (a), users
should trust a claim only if it is true. In reality (b), users tend to trust a
reasonable claim and distrust an unreasonable claim. When the truth is
unknown, a good data consumer (c) should be skeptical despite a claims’
plausibility.

Three illustrations showing how users should perceive causal claims in a
perfect world, in reality, and as good data consumers. Read the caption for
the details.

####  6.1.1. Encouraging Skepticism for Reasonable Claims

How do we encourage users to be skeptical about reasonable causal claims
through interface design? Warning could be a potential solution. In study 1,
we observed that participants tended to be more cautious in agreeing with a
reasonable claim given a simple warning message. However, a simple warning
could be unreliable: When the system only provided reasonable causal claims in
study 2, the warning did not seem to promote such caution. To improve the
effectiveness of warning in inspiring skepticism, its design could be improved
based on research in warning science. For example, Wogalter (Wogalter, 2006)
proposed the Communication-Human Information Processing (C-HIP) Model to
describe the perceptual and cognitive processes after people see a warning.
The model suggests asking a series of questions to assess the effectiveness of
warning messages. For instance, do people notice the warning? Is the message
in the warning being effectively communicated?

What are other interface design ideas to help encourage skepticism? In study
1, we found that participants tended to disagree less with unreasonable claims
and agree more with hard-to-tell claims given a scatterplot. This indicates
that correlation depicted in a scatterplot could induce an illusion of
causality. To mitigate this illusion, it seems plausible to hide scatterplots
from causal claims. Nevertheless, some participants felt that they trusted the
system more because the scatterplots enabled them to see the data. Ideally,
designers should keep the benefits of scatterplots while mitigating their side
effects. Ritchie et al. (Ritchie et al., 2019) found that transitioning from a
non-deceptive view to a deceptive one could reduce the deception caused by the
second view while enabling users to access the benefits of first view.
Following this idea, a system could hide scatterplots by default while
providing an option for users to view them. It would be interesting to
investigate whether this design could reduce users’ tendency to confuse
correlation and causation.

How do we improve the general awareness of “correlation is not causation”?
Besides inspiring skepticism about a reasonable claim, warnings also appeared
to raise awareness of “correlation is not causation” in study 1. Again, a
simple warning alone could be ineffective: Even when participants were warned
that correlation is not causation, they tended to agree that the scatterplot
implied causation. This suggests that system developers might need to look
beyond interface design to help users acquire correct statistical knowledge.
Alternatives include pedagogical approaches such as tutorials. For example,
when Tableau introduced Explain Data, they emphasized that users were the data
experts, and they should judge the veracity of the causal claims based on
their knowledge (Tableau, 2019a, b). Future work will study the effectiveness
of such tutorials in reducing users’ tendency to associate correlation with
causation.

####  6.1.2. Encouraging Open-Mindedness for Unreasonable Claims

While this work sheds light on ways to inspire skepticism for reasonable
claims, designs to keep users open-minded when they see unreasonable claims
are yet to be explored. Open-mindedness is a different form of skepticism:
Instead of being skeptical about the automatically generated causal claims,
users are skeptical about their beliefs and expectations about the data.
Different models (e.g., Bayesian statistics (Bolstad and Curran, 2016) and the
data-frame model (Klein et al., 2006a, b)) have been developed to explain the
process through which people update their beliefs. Prior research in
misinformation showed that existing beliefs are rigid, and people are inclined
to resist changes to their beliefs (Lewandowsky et al., 2012).

Although encouraging open-mindedness could be challenging, what are some
potential ideas to keep people open-minded when they see unreasonable causal
claims? An idea is to enable users to tell the system if an answer makes
sense. If users consider an answer questionable, the system could explain why
a causal relationship might exist to prevent users from prematurely rejecting
a causal claim that seems unreasonable. However, further evidence would be
required to demonstrate the effectiveness of this approach.

###  6.2. Study Limitations and Future Work

Our results hint at the potential for scatterplots to create an illusion of
causality and the potential for a simple warning to reduce this illusion. We
note that these are observations under controlled experiments, and we are
prudent in drawing conclusions about the practical significance of the
findings. First, to collect data from hundreds of people on MTurk, we needed
to sacrifice realism to adapt the study for an online setting. For example,
participants examined a series of answers provided by the system rather than
really interact with a working system. Second, collaboration could protect
users from being misled in practice: While an analyst might draw causal
conclusions from correlational evidence, colleagues might remind the analyst
of the flaw. Learning about the practical implications of our findings will
require observing how people employ systems such as Explain Data (Tableau,
2020) in their workflow and studying how people collaborate during data
analysis.

We also note that the effectiveness of a simple warning in reducing causal
illusion warrants further studies. Our work only compared four experimental
conditions (Fig. 4). Evidence from further comparisons (e.g., a comparison
between an additional claim + vis + warning condition and the original claim +
vis condition) could support our findings about the effectiveness of the
warning. An ideal experiment is to consider each information type (claim, vis,
textual description, and warning) an independent variable with two levels
(with and without). This experiment will enable comparisons among all possible
experimental conditions. Nevertheless, adding more conditions greatly reduces
power given Bonferroni correction, and interesting findings might be missed.
In future studies, experimenters would likely want to preserve power by honing
in on a smaller set of comparisons. Our findings could provide guidance on
what focused comparisons to make.

Our target population was potential end users of question-answering systems.
These users include both people who are less proficient in data analysis and
those who are more proficient. Our participants ranged from beginner users to
more advanced analysts and appeared to be a reasonable proxy for our target.
Yet, the focus on these users also implies that some findings (e.g., the
tendency among participants to confuse correlation and causation) may not
generalize if we conduct the studies with professional analysts only. Future
work will replicate our study with these experts.

In both studies, we used a single question to measure trust in the question-
answering system. In future, a questionnaire with multiple questions could be
developed for assessing user trust in these systems. Such a questionnaire will
measure sub-dimensions of trust (e.g., understanding) and enable researchers
to learn about more fine-grained reasons for trusting a system (e.g., the
system is trustworthy because users can easily understand the answers).

We have investigated whether showing correlational evidence could induce
causal illusion. One form of correlational evidence we studied was textual
description about correlation. We note that creating a description that
completely eliminates casual perception could be challenging because people
might easily mistake correlation for causation. Future research will
investigate how different phrasing of correlation descriptions will affect
casual perception.

Our study focused on numerical variables. Visualizing correlation between
numerical variables using scatterplots is common. For other data types (e.g.,
categorical variables), other charts are often used. A natural extension to
our work is to investigate the generalizability of our findings to other data
types and charts.

Finally, although we are advocates for encouraging skepticism when using
question-answering systems, we note that inspiring the right level of
skepticism could be challenging. Ideally, users’ skepticism about a causal
claim should match the evidence they have about the claim: For causal claims
with good support (e.g., carbon dioxide emission leads to global warming),
users could be less skeptical; for claims that lack supportive evidence, users
could evaluate them more critically. However, it is difficult for a system to
infer the amount of evidence users have about a claim and encourage skepticism
accordingly. Moving forward, researchers could investigate whether telling
users to be skeptical (e.g., through warnings) promotes an appropriate level
of skepticism or engenders excessive and unhealthy skepticism.

##  7\. Conclusion

Our work is situated in the discourse about the the ethical implications of
data visualization (Correll, 2019). We highlighted another scenario where
visualizations might mislead users—question-answering systems could visualize
correlation to create an illusion of causality. In particular, we found that
in a system that occasionally provided unreasonable answers, showing a
scatterplot next to a causal claim increased the plausibility of unreasonable
and hard-to-tell claims. However, providing a simple warning about
“correlation is not causation” seemed to lead participants to accept
reasonable claims more cautiously. We further observed that our participants
had a tendency to associate correlation with causation, but the warning
appeared to reduce the tendency. We did not observe these effects of warning
in a system that only provided reasonable answers. Based on the findings, we
advocate that system developers could encourage users to be skeptical about
answers generated by question-answering systems and have proposed ideas for
doing so.

###### Acknowledgements.

We thank Jian Zhao for early comments on the studies and the GT Visualization
Lab for feedback on the paper. Special thanks to the anonymous reviewers for
the thoughtful reviews.

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cost-function markets. In particular, a strictly proper scoring rule $S$
corresponds via convex duality to a convex cost function $C$ such that

$S({\mathbf{p}}_{t},y_{i})-S({\mathbf{p}}_{t-1},y_{i})=({\mathbf{q}}_{t})_{i}-({\mathbf{q}}_{t-1})_{i}-\left[C({\mathbf{q}}_{t})-C({\mathbf{q}}_{t-1})\right].$

(Also, $C$ is differentiable and has a certain set of gradients, namely the
probability simplex.) Here convex duality gives a one-to-one correspondence
between market states ${\mathbf{q}}_{t}$ and predictions ${\mathbf{p}}_{t}$,
such that the trade ${\mathbf{r}}$ with
${\mathbf{q}}_{t}={\mathbf{q}}_{t-1}+{\mathbf{r}}$ is equivalent to the
prediction update ${\mathbf{p}}_{t-1}\to{\mathbf{p}}_{t}$. In other words, the
cost-function interface to the market is equivalent to the scoring-rule
interface.777There is a difference in timing, because as usually implemented,
a cost function collects the cash payment at the time of trade while the
securities pay off at the end; the scoring rule assesses all payments at the
end. For Fact 1, we must either wait to collect the payment until the end, or
assume that one unit of cash has constant utility over time (the usual
assumption).

To complete the sketch, we observe the properties of $C$. Convexity follows
from the convex duality of scoring rules discussed above. Increasing and ones-
invariance follow from Incentive Compatibility, as follows. Because each trade
should correspond one-to-one to a feasible prediction, Incentive Compatibility
implies there must be no available trades that make a guaranteed profit
regardless of the outcome (what the prediction market literature calls an
arbitrage opportunity). Otherwise, traders with many different beliefs would
all choose such a trade. In particular, nonnegative trade bundles must cost a
positive amount of money, which gives that $C$ is increasing. And $\alpha$
units of the grand bundle $(1,\dots,1)$ must cost exactly $\alpha$, since
otherwise buying or short-selling it would guarantee profit; this yields ones-
invariance. ∎

###  B.2. CFMMs elicit ratios of valuations

A consequence of the equivalence is that CFMMs can be viewed as prediction
markets. But what exactly do they “predict”? The answer is: CFMMs elicit
ratios of valuations.

To explain, let us return to the “cashless prediction market”, in which there
are $n$ assets. Instead of payments in cash, each asset had a “price” in units
of the grand bundle $(1,\dots,1)$. Now generalize: consider a market with $n$
arbitrary assets $A_{1},\dots,A_{n}$ with some nonnegative value. One can
similarly use a cost function $C$ to define a cashless automated market maker
with all prices given in units of the grand bundle $(1,\dots,1)$.

When $A_{1},\dots,A_{n}$ are contingent securities (e.g. $A_{i}$ pays off
equal to the number of points scored by team $i$ during the season), this is
known as a _ratio-of-expectations_ market [Frongillo and Waggoner, 2018]. The
market prices reflect the _ratio_ between how the value of $A_{1}$ and of the
grand bundle $A_{1}+\cdots+A_{n}$, because traders pay in units of the latter
to obtain units of the former. However, the market valuation of the grand
bundle itself is never revealed. Similarly, in a CFMM, two traders who value
asset $A_{i}$ at two times asset $A_{j}$ will accept the same pairwise trades,
even if their cash valuations of the grand bundle are very different.

So CFMMs elicit ratios of valuations. For example, at any history $h$, a CFMM
defines via $\mathsf{ValTrades}_{\varphi}(h)$ a truthful mechanism to induce a
single agent to reveal, not their valuation in cash of the assets, but their
ratios of the value of each asset to the value of the grand bundle. (This is
an extension of the fact that CFMMs define proper scoring rules.).

It is not surprising that a CFMM’s prices reflect ratios of valuations of the
assets. What may be surprising is that, _if one designed a market maker with
the intention of eliciting ratios of valuations_ , it would result in a CFMM.
For example, a primary purpose of CFMMs is to “provide liquidity”, i.e. offer
prices to exchange any asset $A_{i}$ for another $A_{j}$. Paying in units of
the grand bundle clearly defeats this purpose, which seems to make a ratio-of-
valuations market a poor choice. However, Incentive Compatibility implies that
a ratio-of-valuations market should be implemented by a cost-function, which
is equivalent to a CFMM that satisfies Liquidation. So good market-making
axioms fall out “for free” from elicitation ones.

##  Appendix C Cashless prediction markets

This section formalizes the equivalence of cost function prediction markets
with and without cash. Recall that, given a cost function $C$, it defines the
following automated market maker on the $n+1$ securities
$A_{1},\dots,A_{n},\$$ (Section 3.1):

$\mathsf{ValTrades}_{C}(h)=\left\\{({\mathbf{r}},\alpha)\mid{\mathbf{r}}\in\mathbb{R}^{n}\right\\}\text{
where $\alpha=C(-{\mathbf{q}}_{h}-{\mathbf{r}})-C(-{\mathbf{q}}_{h})$},$

Then, we defined the cashless version on $n$ securities $A_{1},\dots,A_{n}$ by
Equation 2, reproduced here:

$\mathsf{ValTrades}^{\prime}_{C}(h)=\left\\{{\mathbf{r}}+\alpha\mathds{1}\mid{\mathbf{r}}\in\mathbb{R}^{n}\right\\}\text{
where $\alpha=C(-{\mathbf{q}}_{h}-{\mathbf{r}})-C(-{\mathbf{q}}_{h})$}.$

First, we observe that any valid history and valid trade in the cashless
market is a special case of a valid history and trade in the original market

###### Proposition 0.

Consider the cashless version of the market with a valid history $h$, reserves
${\mathbf{q}}_{h}$, and valid trade
${\mathbf{r}}\in\mathsf{ValTrades}^{\prime}_{C}(h)$, resulting in history
$h^{\prime}$ with reserves
${\mathbf{q}}_{h^{\prime}}={\mathbf{q}}_{h}+{\mathbf{r}}$. Then there is a
valid history of the original prediction market reserves
$({\mathbf{q}}_{h},0)$ and valid trade $({\mathbf{r}},0)$, resulting in
reserves $({\mathbf{q}}_{h^{\prime}},0)$. Furthermore, for any realization
$Y=y_{i}$, that trade’s net payoff is the same in both markets.

###### Proof.

Because ${\mathbf{r}}\in\mathsf{ValTrades}^{\prime}_{C}(h)$, we can write
${\mathbf{r}}={\mathbf{r}}^{\prime}+\left(C(-{\mathbf{q}}_{h}-{\mathbf{r}}^{\prime})-C(-{\mathbf{q}}_{h})\right)\mathds{1}$.
By ones-invariance,
$C(-{\mathbf{q}}_{h}-{\mathbf{r}})=C(-{\mathbf{q}}_{h}-{\mathbf{r}}^{\prime})-C(-{\mathbf{q}}_{h}-{\mathbf{r}}^{\prime})+C(-{\mathbf{q}}_{h})=C(-{\mathbf{q}}_{h})$.
So if $({\mathbf{q}}_{h},0)$ is a valid set of reserves in the original
market, then $({\mathbf{r}},0)$ is a valid trade in the original market. Now
by induction on the history, we get that valid histories in the two markets
correspond. Furthermore, the net payoff to the trader for the trade in either
market, under any outcome $Y=y_{i}$, is $-{\mathbf{r}}_{i}$, since the cash
transfer is zero in the original market. ∎

Now, define $f:\mathbb{R}^{n+1}\to\mathbb{R}^{n}$ by
$f({\mathbf{b}},a)={\mathbf{b}}+a\mathds{1}$. We can show the more interesting
direction of the equivalence as follows:

###### Proposition 0.

Consider a cost-function market with valid history $h$, current reserves
${\mathbf{q}}_{h}$, and valid trade
$({\mathbf{r}},\alpha)\in\mathsf{ValTrades}_{C}(h)$, resulting in history
$h^{\prime}$ with reserves
${\mathbf{q}}_{h^{\prime}}={\mathbf{q}}_{h}+(-{\mathbf{r}},\alpha)$. Then in
the cashless version, there is a valid history with reserves
$f({\mathbf{q}}_{h})$ and valid trade $f({\mathbf{r}},\alpha)$, resulting in a
history with reserves $f({\mathbf{q}}_{h^{\prime}})$, such that for any
outcome $Y=y_{i}$, the trade’s net payoff is the same as in the original
market.

###### Proof.

If $({\mathbf{r}},\alpha)\in\mathsf{ValTrades}_{C}(h)$, then
$\alpha=C(-{\mathbf{q}}_{h}-{\mathbf{r}})-C(-{\mathbf{q}}_{h})$. Suppose for
now $f({\mathbf{q}}_{h})$ are valid reserves in the cashless market. Then the
trade $f({\mathbf{r}},\alpha)={\mathbf{r}}+\alpha\mathds{1}$, with
$\alpha=C(-{\mathbf{q}}_{h}-{\mathbf{r}})-C(-{\mathbf{q}}_{h})$, proving that
it is a valid trade in the cashless market. Now by induction on the history,
we get that valid histories in the two markets correspond. Furthermore, the
net payoff to the trader for the trade in either market, under any outcome
$Y=y_{i}$, is $-{\mathbf{r}}_{i}-\alpha$. In the original market, this occurs
because the trader pays $\alpha$ units of cash and additionally gives the
bundle ${\mathbf{r}}$, which results in $-{\mathbf{r}}_{i}$ when $Y=y_{i}$. In
the cashless market, this occurs because the traders gives the bundle
${\mathbf{r}}+\alpha\mathds{1}$, which results in $-{\mathbf{r}}_{i}-\alpha$
when $Y=y_{i}$. ∎