chapter ten
10 Potential Outcomes Framework
This chapter covers
- Explaining potential outcomes and their assumptions.
- Understanding the relationship between potential outcomes and DAGs.
- Using potential outcomes in the adjustment formula and instrumental variables.
There are two main frameworks in causal inference: one based on graphs (DAGs) and another called Potential Outcomes (PO), also known as the Rubin Causal Model. These two frameworks are valid and consistent with each other, allowing you to choose the one that suits your situation. We’ve spent Parts 1 and 2 on DAGs, and now in Part 3, we will get a taste of POs.
Now, you might be asking yourself, why bother learning a new framework? There are three reasons:
- A significant amount of literature is written using the PO framework, especially in econometrics, biostatistics and epidemiology. So, if you come across it, understanding PO becomes essential.
- The PO notation operates at an individual level, making it more convenient for managing individual effects. Moreover, it is well-suited for dealing with counterfactuals, as expressing them in the do notation can be cumbersome.
- Looking at the same concepts from a different perspective will help you assimilate the contents covered so far.
In the first section, we will introduce the PO notation and explain how to define the Average Treatment Effect (ATE) using this new notation. Additionally, we will outline the fundamental assumptions required for the proper use of PO.