BIOST 881: Advance Topics in Causal Inference

Book:

What If by Migel A. Hernán and James M. Robins (HR)

Lecture Slides

0. Course Overview

1. Introduction and Potential Outcomes

Reading: HR Chapters 1-3

2. DAGs, Confounding, Backdoor Criterion

Reading: HR Chapters 6,7

Richardson and Robins (2013). Single World Intervention Graphs (SWIGs): A Unification of the Counterfactual and Graphical Approaches to Causality

Verma and Pearl (1988). Causal Networks: Semantics and Expressiveness

Geiger, Verma, and Pearl (1990). Identifying independence in bayesian networks.

VanderWeele (2019). Principles of confounder selection.

Cineli, Forney, and Pearl (2020) A crash course in good and bad controls.

3. Selection Bias, Non-Compliance, and Measurement Error

Reading: HR Chapters 8,9

Hernán, Hernández-Díaz, Robins (2004). A Structural Approach to Selection Bias

Bareinboim, Tian, and Pearl (2014). Recovering from Selection Bias in Causal and Statistical Inference

Hernán and Hernández-Díaz (2012) Beyond the intention-to-treat in comparative effectiveness research

4. Effect Modification, Interaction, and Collapsibility

Reading: HR Chapters 4,5

Greenland, Robins, and Pearl (1999). Confounding and Collapsibility in Causal Inference

Lecture Slides from Past Years