PBHLTH 252D
Introduction to Causal Inference
With the ongoing “data explosion”, methods to delineate causation from correlation are perhaps more pressing now than ever. This course will introduce the Causal Roadmap, which is a general framework for Causal Inference. The statistical methods include G-computation, inverse probability weighting (IPW), and targeted machine learning estimation (TMLE) with Super Learner, an ensemble machine learning method. The emphasis will be on practical implementation and real-world challenges and solutions. Through the course, students will gain experience working through the Roadmap with case studies, R labs, R assignments, and a final project using real data.
Topics
- Translate your research question and knowledge into a causal model (directed acyclic graphs and non-parametric structural equation models) Define the target causal estimand with counterfactuals Assess identifiability of the target causal effect and express it as a parameter of the observed data distribution (i.e., a statistical estimand/parameter) Explain the challenges posed by parametric estimation approaches and apply machine learning methods
Official catalogue course objectives and student learning outcomes
Sources
- Course catalogue ↗2026-2027