1 Causal Inference and Potential Outcomes

This week we will introduce the topic of causal inference. We will outline a specific definition of causality using the potential outcomes framework, and will describe the fundamental problem of causal inference. We will highlight the persistent threat of selection bias in observational data and we will discuss differences between statistical inference and causal inference.

There are excellent introductions to the potential outcomes framework in Angrist and Pischke, 2014, (Introduction) and in Gerber and Green, 2012 (Chapter 1). The Paul Holland article on Statistics and Causal Inference provides an excellent history of the framework, and relates the conception of causality we focus on to long-standing philosophical discussions. If you are looking for inspiration, and a fun read, then the first half of David Freedman’s article on Statistical Models and Shoe Leather (p. 291-300) provides an interesting history of John Snow’s (no, not the guy from Game of Thrones) study of cholera in 19th century London. The second half of that article is also worth reading, as a fairly strong argument for why “statistical technique can seldom be an adequate substitute for good design, relevant data, and testing predictions against reality in a variety of settings.”

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