MY457 Half Unit
Causal Inference for Observational and Experimental Studies
This information is for the 2023/24 session.
Dr Daniel De Kadt
This course is available on the MSc in Applied Social Data Science, MSc in Behavioural Science, MSc in European and International Public Policy, MSc in Human Geography and Urban Studies (Research), MSc in International Social and Public Policy (Research), MSc in Political Science (Political Science and Political Economy), MSc in Social Research Methods, MSc in Statistics, MSc in Statistics (Financial Statistics), 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 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 or equivalent. Familiarity with notions of research design in the social sciences, to the level of MY400 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, 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 WT.
This course has a reading week in Week 6 of WT.
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.
Exam (100%, duration: 2 hours) in the spring exam period.
Total students 2022/23: 39
Average class size 2022/23: 20
Controlled access 2022/23: No
Lecture capture used 2022/23: Yes (LT)
Value: Half Unit
Course selection videos
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