MY457 Half Unit
Causal Inference for Observational and Experimental Studies
This information is for the 2020/21 session.
Teacher responsible
Dr David Hendry
Availability
This course is available on the 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 Political Science and Political Economy, MSc in Social Research Methods, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (ÐÓ°ÉÂÛ̳ 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 as an outside option to students on other programmes where regulations permit.
Pre-requisites
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 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.
Teaching
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.
Assessment
Exam (100%, duration: 2 hours) in the summer exam period.
Important information in response to COVID-19
Please note that during 2020/21 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 situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of 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 2019/20: 50
Average class size 2019/20: 17
Controlled access 2019/20: No
Value: Half Unit
