Causal Inference in the Social Sciences

February 27 – March 2, 2012

Lecturers:

Prof. Dr. Ben Jann, University of Bern, Switzerland

Rudi Farys, University of Bern, Switzerland

Abstract:

Based on the potential outcomes notation of causal effects (a.k.a. the Rubin Causal Model) a variety of methods for causal inference from observational data have been developed over the last two decades and became increasingly popular in cutting-edge social science research. This course provides an introduction to these methods, explains their foundations and assumptions and discusses the conditions under which their use is appropriate. Topics covered are conceptional approaches such as the potential outcomes framework and directed acyclic graphs (DAG) as well as estimation methods such as matching, propensity-score reweighting, instrumental variables, regression discontinuity, and difference-in-difference. Upon completion of this course, students should have acquired skill in the estimation, specification and diagnostics of the various methods and gained hands-on experience with those methods through the use of appropriate software and actual data sets. For the exercises in the computer lab, the course relies on Stata and R.

Basic reading:

• Angrist, J. D. & J.-S. Pischke (2009). Mostly Harmless Econometrics. Princeton.
• Imbens, G. W. & J. M. Wooldridge (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47: 5-86.
• Morgan, S. L. & Ch. Winship (2007). Counterfactuals and Causal Inference. New York.