Causal Inference in the Social Sciences: Matching, Propensity Scores, and Other Strategies (Berkeley, CA)
- Dominik Hangartner, London School of Economics
- Marco Steenbergen, University of Bern
This course provides an introduction to statistical methods used for causal inference in the social sciences. Using the potential outcomes framework of causality, we discuss designs and methods for data from randomized experiments and observational studies. In particular, the designs and methods covered in the course include (propensity score) matching, instrumental variables, difference-in-difference, synthetic control, and regression discontinuity. Examples are drawn from across the social science disciplines.
The course will use the R statistical computing environment for computation.
NOTE: Prerequisites for the course are knowledge of multiple regression using linear algebra and some familiarity with limited dependent variables.
Fee: Members = $1500; Non-members = $3000
Location: University of California, Berkeley -- Berkeley, CA
Date(s): July 14 - July 18
Time: 9:00 AM - 5:00 PM