written by Roni Kobrosly on 2022-08-03 | tags: scipy causal inference
I recently had the privilege of giving a talk and tutorial session at SciPy 2022 in Austin. Besides rediscovering how hot central Texas is in the summer (the sun is trying to kill you), I walked away with some useful insights from the audience such as: (1) people are hungry to learn more about this topic. (2) Most people came in thinking that causal inference was a way to improve ML predictions, rather than more closely related to A/B testing and decision science. (3) One of the more controversial points was around how people should not interpret variable importance measures (e.g. SHAP values) as causal. You can watch the first half of the talk here and if you'd like to look through the materials / try your hand at the execises, you can find them here on GitHub.