Causal Inference for the Social Sciences
- Jake Bowers, University of Illinois at Urbana-Champaign
- Ben Hansen, University of Michigan
This course provides an introduction to statistical methods used in causal inference. The content is geared specifically toward students and researchers in the social sciences. Using the potential outcomes framework of causality, topics covered include research designs such as randomized experiments and observational studies. Classes will explore the impact of noncompliance in randomized experiments, as well as nonrandom assignment in observational studies. To analyze these research designs, the methods covered include matching, instrumental variables, difference-in-difference, and regression discontinuity. Examples are drawn from economics, political science, public health and sociology.
Prerequisites for the course are knowledge of multiple regression using linear algebra and some familiarity with limited dependent variables. The course will rely on R for computation.
Fees: Consult the fee structure.
Tags: Causal Inference
Location: ICPSR -- Ann Arbor, MI
Date(s): July 21 - August 15
Time: 3:00 PM - 5:00 PM