MY557 Half Unit
Causal Inference for Observational and Experimental Studies
This information is for the 2017/18 session.
Dr David Hendry
Available to all research students.
Knowledge of multiple linear regression and some familiarity with generalised linear models, to the level of MY452/MY552 or equivalent. Familiarity with notions of research design in the social sciences, to the level of MY400/MY500 or equivalent
This course provides an introduction to statistical methods used for causal inference in the social sciences. Using the potential outcomes framework of causality, topics covered include research designs such as randomized experiments and observational studies. We explore the impact of noncompliance in randomized experiments, as well as nonignorable treatment 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 different social sciences. The course includes computer classes, where standard statistical computer packages (Stata or R) are used for computation.
20 hours of lectures and 10 hours of computer workshops in the LT.
There will be no lectures or computer classes in Week 6 of term.
Exercises from the computer classes can be submitted for marking.
Rosenbaum, PR. (2010). Design of Observational Studies. Springer;
Angrist, J. D. and Pischke, J.-S. (2009). Mostly Harmless Econometrics.
Princeton University Press.
Coursework (100%, 4000 words).
Total students 2016/17: 4
Average class size 2016/17: 1
Value: Half Unit