I am Terrance Law, and I am a PhD student at Georgia Tech
Today, I will be talking about how question-answering systems that provide causal claims
can present evidence about correlation to mislead people to make conclusions about causation
You can find the pdf of our paper on my website: terrancelaw.github.io
Here is Tableau Explain Data
With Explain Data, users can ask why there is an extreme observation in their data
And Explain Data will conduct causal inference and provide potential explanations for the extreme data observation
Causal inference from observational data is inherently challenging
There is some chance that the claims made by Explain Data is a false conclusion
If users believe in what the system says,
they will misinterpret the data and draw wrong conclusions about the data
Motivated by this observation, we had two research questions
Do users misinterpret a system’s causal claims?
And If yes, how do we mitigate the misinterpretation?
To address these questions, we conducted three crowdsourced studies
As a first step, we conducted a pre-study to collect causal claims of different levels of plausibility
A casual claim may be something like
Low employment rate in Mississippi may be a factor that leads to high poverty rate in Mississippi
In study 1, we presented participants with a series of why questions and answers to these why questions
For example, the why question might be “Why is the poverty rate in Mississippi so high?”
And the system’s answer might be a causal claim like
“Low employment rate in Mississippi may be a factor that leads to high poverty rate in Mississippi.”
These causal claims were gathered from the pre-study
For each question, we asked participants to what extent do they agree with the causal claim
For study one, we also wanted to mimic typical question-answering systems
that tend to be unreliable in answering why questions
So, some causal claims were reasonable
some were unreasonable
and some were difficult to judge whether they were reasonable or not
To learn what kinds of design led to the most user misinterpretation,
we presented the answers with different designs
In the first design, there is only a causal claim
The second designs present a scatterplot as a visual evidence about correlation
In the third design, we further added a textual description that describes the correlation between the two variables
In the final design, there is a message that warns participants that
although it seems that there is a correlation between the two variables, correlation is not causation.
So, what did we find from study 1?
We found that the when the scatterplot was presented to show evidence about correlation,
there was a higher likelihood that people would agree with unreasonable claims
And with the warning message, people were less likely to agree with reasonable claims,
but we didn't see this effect for unreasonable causal claims
It meant that warning was somewhat effective in mitigating the misinterpretation of correlation as causation
After study 1, we also conducted a study 2
Study 2 was the same as study 1
However, instead of seeing causal claims with a different level of plausibility,
in study 2, participants only saw reasonable causal claims
In study 1, we found that the warning message made people more cautious in accepting some causal claims
But in study 2, we were not able to see any effects
The warning message did not make participants more cautious in accepting the claims
Let’s go back to our questions and see what we have learnt
By presenting visual evidence about correlation like a scatterplot,
these systems could increase the likelihood for people to accept causal claims
even when they do not sound reasonable
And a simple warning that correlation is not causation
is not always effective in increasing people’s awareness that correlation is not causation
They tell us that when question-answering systems do not provide reliable answers,
these systems could be designed in ways to encourage skeptism.
For the ideas for designing question-answering systems that encourage skeptism,
you can learn more from our paper at terrancelaw.github.io
With that, I'd like to thank my collaborators, and thank you for watching this talk