Statistical and AI Methods for Causal Inference

Course Director: Himel Mallick, PhD

The goal of this course is to introduce a core set of modern statistical and AI-based concepts and techniques to students, and to demonstrate how to use them to answer complex research questions in healthcare. Students will acquire knowledge of causal inference methods that integrate statistical modeling with machine learning and artificial intelligence, including potential outcomes, counterfactuals, directed acyclic graphs, non-parametric structural equation models, inverse probability weighting, g-computation, causal mediation analysis, causal multimodal AI, and precision medicine.