Inspiration
Cause and effect relationships drive some of the most important decisions businesses and researchers make. Causal graphs show how to infer causation amidst a network of potential confounders.
What it does
The user can design a graph matching their scenario, with each vertex representing a variable. Through a C++ backend, our web tool then identifies which set of vertices the user should control for when statistically measuring the cause-and-effect relationship between the first two vertices entered.
How we built it
We used a Python front end and a C++ backend.
Challenges we ran into
The Python front it was quite hesitant to listen to the C++ backend, requiring us to switch to Linux.
Accomplishments that we're proud of
The backend runs faster than we initially estimated.
What we learned
We learned that lesser known algorithms are sometimes more complicated then they first appear.
What's next for Web Tool for Visualizing Causal Directed Acyclic Graphs
We hope to update the app to solve a few bugs plaguing larger graphs.
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