I’ve been puzzling through how best to teach a research design and causal inference course to poli sci grad students. I want to catch the big group of dissertation-writers who are using data and statistics but haven’t been trained as statisticians. There’s a lot of important skills and subjects for a political scientist to master, and stats sometimes falls by the wayside until it’s too late.
I’ve settled on the style in which the likes of David Card, Ken Chay, and David Lee taught me Labor Economics. They had this wonderful method of teaching by example: making us read seminal papers, write review reports, and spend seminar time finding the fatal flaws that pushed econometric science forward.
If I were satisfied with papers on training programs, labor supply, and minimum wages, my syllabus would be set. But I’m hoping to fill the syllabus with papers on crime, riots, war, elections, democratization, policy-making, and the like.
My bleg is for papers that fit the following criteria:
Important in its time (and perhaps even now);
Illustrate an econometric technique for understanding causality;
Have a fatal flaw–a weak instrument, an omitted variable, a terrible measurement error problem–that we only recognize in retrospect; and
The flaw isn’t so obvious that a second year grad student would catch it.
I’ll post the final syllabus later this week.