- So hi everyone, good afternoon.
My name is Eric Yen, I'm from UIUC.
Today I'm happy to present our short paper,
An Intelligent Assistant for Mediation Analysis
in Visual Analytics.
So over these years,
visual analytics has become increasingly popular
because it is very useful and effective to use
interactive data visualizations to facilitate the analysis.
Nowadays,
many easy-to-use visual analytics tools are available
for anyone to quickly plot the visualization
and discover interesting trends.
However,
when it comes to causal inference,
there is one challenge:
the correlations shown on the visualizations
does not necessarily imply the causation.
Take an example:
If we measure the shoe size of kids and their math skills,
we will find there is a very strong positive correlation,
but it is obvious that there is no causation here.
Giving you a case,
larger shoes doesn't automatically improve
their math skills.
It's just because older kids have larger shoes
and they have better math skills in general.
This example is intuitive to understand
but in many cases it is hard to determine
whether causation exists.
For example,
if someone collects admission data of a university,
and find out that the male students
have a higher admission rate than the female students.
Now it is tempting to accuse the university
that there exists a gender bias.
However the correlation might be mediated
by some other variables.
Maybe there is no gender bias in truth.
This kind of causal inference is
within the scope of mediation analysis,
which tries to explain the causal relationships
among three variables.
Using our example we're trying to explain
why gender have different admission rates
by a hypothesis saying that
maybe it is because the female applicants tend to apply
to more competitive departments,
and those departments have lower admission rates in general.
Is there any visual patterns to consider?
So our goal in this paper
is to try to design an visual analytics interface
that assists in making causal inferences
in this kind of mediation analysis.
Next I'm going to discuss our design rationales
and our actual interface design
in an exploratory user study.
What are the important patterns we need to see
in this scenario?
Using our same example,
we have three variables here.
To generalize,
we use x and y to represent the two variables
of our target relationship,
and use m to represent the variable
that could be the possible mediator.
The first thing we can check
is whether this pairwise correlation exists.
For example between x and y,
do female applicants really have lower admission rates?
And between x and m,
are those female applicants,
do apply more to some departments
and between m and y,
whether do those departments
really have lower admission rates?
However, even if we find all this correlations exist,
it is still possible that m does not influence y.
In this example the gender now is a confounding factor.
And even if m does not influence y,
the confounding effects still makes us feel
there is correlation.
In statistics,
to solve this problem we can run a regression
to fit y using both x and m.
If m still has an effect on y when x is controlled,
then we can be more confident that m actually influences y.
Using visualization, we can do a similar thing,
by plotting the visualization
using all the three variables at once.
We can partly separate the data using x variable
and to check whether the m-y relation still exist
within each x subgroup.
In this case,
we compare whether the m-y correlation still exists
when x is controlled.
Similarly,
we can also see whether the x-y correlation still exist
when m is controlled.
This leads to high-level design rationales.
First, the three-variable visualization is necessary.
And second,
a consistency of status of the correlations m-y
and x-y are important.
So to help people discover
this important visualization patterns,
we designed a visual analytics interface
that will automatically recommend
related important visualizations
and help to detect a consistent or inconsistent trend.
And also provide visual cues to help people interpret.
Here's a screenshot of our interface:
On the left part,
it shows the variables that are being analyzed.
Users can click on the variables to plot a visualization
which is shown in the middle.
Then in the right part,
the system automatically recommends important visualizations
based on what is shown in the middle.
To find the recommended visualizations and
provide visual cues,
we run several regression tests in the background.
A regression test are used to test
whether the current correlation being shown is significant
and whether it is consistent
when another variable is being controlled.
For example,
if the user select x and y variable,
we will regress y and x to test whether it's significant,
and also regress y on both x and m
to see whether it is consistent.
These additional regressions corresponds
to the three-variable visualization
and therefore will be recommended in the interface,
just like in our example here.
The user selects a department and admission variables,
and in the middle,
a visualization shows that one of our departments
indeed has a lower admission rate.
On the right,
we recommend the three-variable visualization
and also provide visual cues like the arrows here,
based on our statistical tests.
The green arrows indicate a trend if detected
and the flat horizontal black arrows there
means that there is no strong correlation.
We try to bring the user's attention to compare
these conflicting visual patterns
and this can help them to distinguish
what is the actual causal model behind this.
Here, we did an exploratory user study
to get early feedback of our design.
We conducted a within-subject study
with 5 graduate students.
The initial results suggest that,
with our assisting features
they might make fewer wrong inferences
and be more confident about their answers.
In the interviews the participants mentioned
that the ability to compare conflicting visualizations
side by side
and chart important scores
helps them prioritize their reasoning effort.
However this is just a small-size study
so further user studies are required
to actually test the effects of our interface.
In conclusion
we designed an interface that assists in mediation analysis
in visual analytics.
The main interface features include
detecting consistent or inconsistent trends
shown in the visualizations,
and also provide visual cues
to help them interpret visualizations.
This will be the end of my talk.
Thank you very much.
(applause)
- [Announcer] Are there any questions?
- [Audience Member] So what is the future work?
- When it is only for three variables by now,
but of course we want to extend the model
to incorporate more variables
and also where now we only use regressions
maybe when more variables are being analyzed,
maybe like path analysis or Bayesian networks
this kind of statistical framework
can be incorporated into this system.
- [Announcer] Excellent, thank you.
Any more questions?
If not, one, two, three.
Let's thank the speaker again.
(applause)