Linear Regression and Causality for Neophytes

(This is an update on a post I had initially written at the start of the academic year. I figured it would come in handy, as many of us are busy writing our syllabi for the spring semester.)

If you teach in a policy school, a political science department, or in an economics department that grants Bachelor of Arts instead of Bachelor of Science degrees, chances are some of your students are not quite conversant in the quantitative methods used in the social sciences.

Many of the students who sign up for my fall seminar on the Microeconomics of International Development Policy or for my spring seminar on Law, Economics and Organization, for example, are incredibly bright, but they are not familiar with regression analysis, and so they don’t know how to read a regression table.

This makes it difficult to assign empirical papers in World Development for in-class discussion, let alone empirical papers in the Journal of Development Economics.

While I do not have the time to teach basic econometrics to students in those seminars, I have prepared two handouts for them to read in preparation for reading papers containing empirical results, which I thought I should make available to anyone who would rather not spend precious class time teaching the basics of quantitative methods. I have used both these handouts in my development seminar last fall, and my students said that they had learned quite a bit from reading them.

The first handout is a primer on linear regression, which shows analytically and graphically (and hopefully painlessly) what a regression does, and why it is such a useful tool in the social sciences. Perhaps more importantly, this handout also explains how to read a regression table.

The second handout primer on the identification of causal relationships in the social sciences, which discusses the distinction between correlation and causation and explains two ways in which social scientists go about making causal statements (i.e., randomized controlled trials and instrumental variables), with a few examples. I suggest supplementing this handout with a reading of Jim Manzi’s “What Social Science Does–and Doesn’t–Know” in City Journal as well as with Esther Duflo’s TED talk.

Of course, neither handout is a substitute for a course in econometrics or on research design, respectively, but these handouts are intended primarily for undergraduates or Masters students with little to no quantitative background.

(Update: This post by Tom Pepinsky also offers a very good introduction to the identification of causal relationships. HT to Chris Blattman for this great find.)