MY557      Half Unit
Causal Inference for Observational and Experimental Studies

This information is for the 2021/22 session.

Teacher responsible

Prof Jouni Kuha COL.8.04


This course is available on the MPhil/PhD in Economic Geography, MPhil/PhD in Environmental Economics, MPhil/PhD in International Relations, MPhil/PhD in Regional and Urban Planning Studies, MPhil/PhD in Social Research Methods, MRes/PhD in Management (Employment Relations and Human Resources), MRes/PhD in Management (Marketing), MRes/PhD in Management (Organisational Behaviour) and MRes/PhD in Political Science. This course is available as an outside option to students on other programmes where regulations permit.

This course is not controlled access. If you register for a place and meet the prerequisites, if any, you are likely to be given a place.


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

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 randomised experiments, observational studies, and so-called natural experiments. We explore the impact of noncompliance in randomized experiments, as well as nonignorable treatment assignment in observational studies. To analyse these research designs, the methods covered include experiments, matching, instrumental variables, difference-in-difference, and regression discontinuity. Examples are drawn from different social sciences. The course includes computer classes, where the R software is used for computation.


This course is delivered through a combination of classes and lectures totalling a minimum of 20 hours across Lent Term. This year, the lectures may be delivered live or as short online videos. The classes will be live and in person, and delivered online or in class. This course has a reading week in Week 6 of LT.

Formative coursework

Exercises from the computer classes can be 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.

Holland, Paul W. “Statistics and Causal Inference.” Journal of the American Statistical Association 81(396): 945-960.


Coursework (100%, 4000 words).

Course selection videos

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.

Important information in response to COVID-19

Please note that during 2021/22 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the differing needs of students in attendance on campus and those who might be studying online. For example, this may involve changes to the mode of teaching delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.

Key facts

Department: Methodology

Total students 2020/21: 11

Average class size 2020/21: 3

Value: Half Unit

Guidelines for interpreting course guide information