3 Consistency and positivity

3.1 Exchangeability in Observational Studies

Sep 16. Slides After class, read Hernán and Robins 2020 Chapter 3.4-3.5. Optionally, read Hernán 2016.

What makes causal inference with observational data so challenging? Why is making treatment precise so important? These are the topics we’ll discuss in this lecture!

3.2 Lab: Causal inference with interference

Sep 17 Discussion and discussion slides

When defining causal effects, we often discuss the outcome \(Y^a\) that a person would realize if they were exposed to treatment value \(a\). But definitions become harder if there exists interference: the outcome of unit \(i\) depends on the treatment assigned to unit \(j\). This discussion will focus on understanding interference and why we need to update our potential outcomes notation if interference is present.