Part 2: A Selected History of Quantitative Causal Inference
The Extraordinary Role of Data Workers and Data Theorists at Princeton and Harvard
In a previous substack, I discussed the emergence of the counterfactual idea for defining causal effects to John Stuart Mill and Jerzy Neyman’s 1923 article. I then noted how Ronald Fisher, reading Neyman’s article, immediately put two and two together and deduced that if you physically randomized treatments, you could solve the causal inference problem that Mill and Neyman had introduced with their counterfactual definitions. In this next part, I want to fast forward to the late 20th century and discuss the development of “causal inference methodologies” within labor economics and econometrics by discussing Princeton’s Industrial Relations Section, and it’s many talented labor economists, as well as two economists at Harvard’s economics department and their collaborator in the statistics department. This had previously been one very long article, but I decided to break it up into two parts to make it a little more palatable to the reader. Plus some people probably just want to see …
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