Modern Methods for Causal Inference

Course Director: Himel Mallick, PhD

The goal of this course is to introduce a core set of modern statistical concepts and techniques to the students, and to demonstrate how to use them to answer complex research questions in healthcare. The students will acquire knowledge on causal inference methods using machine learning, including directed acyclic graphs, non-parametric structural equation models, inverse probability weighting, g-computation, survival analysis, marginal structural models, longitudinal data, mediation analyses, effect modification, and precision medicine. This course will use the free software R to perform all statistical analysis.