Causal Inference with Causallib

Abstract

Imagine the newest medical prediction algorithm is claiming you have high-risk for some health condition. I bet the first thing going through your mind is "well, what can I do to reduce it". Regular prediction is not always enough, we often care about predicting the consequences of several paths of action we can take - the causal effect of these actions. In this talk I will briefly present causal inference - the science of estimating causal effect of actions using observational data and how it differs from regular prediction. I will overview models for estimating causal effect and how to apply them with causallib - a one-stop-shop open-source Python package for flexible causal inference modeling.