3.1 — The Fundamental Problem of Causal Inference & Potential Outcomes — Readings

Strongly Recommended

• [Ch. 1] in Michael A BaileyReal Econometrics (New York: Oxford University Press, 2017).
  
  
• Ch. 4 in Cunningham (2020), Causal Inference, the Mixtape
• Rubin Causal Model

Bailey begins the book with a discussion of causality and random control trials that is pretty good.

The potential outcomes notation (e.g. \(Y_i^{1}\) and \(Y_i^{0})\) and model comes from a very famous 1974 paper by Donald Rubin in psychology. You can read more about it in Cunningham (2020) above, or the Wikipedia entry on the model.

Scott Cunningham’s excellent (and free!) Causal Inference, the Mixtape has a great discussion of the history, and examples, of potential outcomes in an accessible way.

The classic example that most economists (including myself) were taught about causality is the treatment of the Rubin model in Angrist and Pischke’s Mostly Harmless Econometrics (one of the classic books on econometrics). You do not need to buy that book for this class, but if you will be doing data work in your future, or going to graduate school, this book is a must own and read:

• Angrist and Pischke, 2009, Mostly Harmless Econometrics

My health insurance example is lifted directly out of this book.

Here’s also a great list of famous social science (including economics) papers that use natural experiments:

• List of 19 Natural Experiments

For more on John Snow and the birth of epidemiology, the excellent PBS show Victoria has a full episode (and great resources) about the cholera outbreak.