Tutorial: A practical introduction to causal inference

Abstract

This tutorial will consist of a practical introduction to the estimation of causal effects. We will experiment with the concepts of average treatment effect, randomization, covariate adjustment, and inverse probability weighting to derive common estimators from the literature. We will also see where machine learning models fit into such estimators. Formal derivations will be presented and supported by extensive visualizations.

Alexandre Drouin

Staff Research Scientist

My research interests include machine learning, causal inference, and computational biology.