MY557 Half Unit
Causal Inference for Observational and Experimental Studies
This information is for the 2022/23 session.
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
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.
Combined hours across lectures and classes will be equivalent to a minimum of 30 hours of face-to-face teaching across the LT.
This course has a Reading Week in Week 6 of LT.
Exercises from the computer classes can be submitted for feedback.
• Imbens, G. W. and Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press.
• Hernán, M. A. and Robins, J. M. Causal Inference: What If.
• Angrist, J. D. and Pischke, J.-S. (2009). Mostly Harmless Econometrics. Princeton University Press.
• Rosenbaum, P.R. (2010). Design of Observational Studies. Springer.
Coursework (100%, 4000 words).
Total students 2021/22: 11
Average class size 2021/22: 5
Lecture capture used 2021/22: Yes (LT)
Value: Half Unit
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