My program evaluation class is basically a fun wrapper around topics in causal inference and econometrics. I’m a big fan of Judea Pearl-style “causal revolution” causal graphs (or DAGs), and they’ve made it easier for both me and my students to understand econometric approaches like diff-in-diff, regression discontinuity, and instrumental variables.

(See this notebook on GitHub) A year ago, I wrote about how to use R to solve a typical microeconomics problem: finding the optimal price and quantity of some product given its demand and cost. Doing this involves setting the first derivatives of two functions equal to each other and using algebra to find where they cross.

(See this notebook on GitHub) Taking a sample from two groups from a population and seeing if there’s a significant or substantial difference between them is a standard task in statistics.