MY457      Half Unit
Causal Inference for Observational and Experimental Studies

This information is for the 2019/20 session.

Teacher responsible

Dr David Hendry


This course is compulsory on the MSc in Political Science and Political Economy. This course is available on the MPhil/PhD in Accounting, MSc in Applied Social Data Science, MSc in Behavioural Science, MSc in Human Geography and Urban Studies (Research), MSc in International Social and Public Policy (Research), MSc in Social Research Methods, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (LSE and Fudan), MSc in Statistics (Financial Statistics) (Research), MSc in Statistics (Research), MSc in Statistics (Social Statistics) and MSc in Statistics (Social Statistics) (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.


Knowledge of multiple linear regression and some familiarity with generalised linear models, to the level of MY452 or equivalent. Familiarity with notions of research design in the social sciences, to the level of MY400 or equivalent.

Course content

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.

Formative coursework

Exercises from the computer classes are submitted for feedback.

Indicative reading

Angrist, J. D. and Pischke, J.-S. (2009). Mostly Harmless Econometrics. Princeton University Press. Rosenbaum, P.R. (2010). Design of Observational Studies. Springer.


Exam (100%, duration: 2 hours) in the summer exam period.

Key facts

Department: Methodology

Total students 2018/19: 48

Average class size 2018/19: 16

Controlled access 2018/19: No

Value: Half Unit

Guidelines for interpreting course guide information