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BAEcon Economics and Social Statistics / Course details
Year of entry: 2021
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Course unit details:
Quantitative Evaluation (of Policies, Interventions and Experiments)
|Unit level||Level 3|
|Teaching period(s)||Semester 2|
|Offered by||School of Social Sciences|
|Available as a free choice unit?||Yes|
Researchers, government, policy takers, business leaders and people in general are motivated by "causal questions" of the type "Does X cause Y" (e.g does policing reduce crime? Do minimum wages increase unemployment? Does a new educational innovation increase educational achievement? Does a new policy reduce waiting lists in hospitals? Does expenditure in marketing increase sales? Dos affirmative action reduce discrimination?). Standard statistical methods, regardless of their complexity, cannot answer these questions on their own and a new set of statistical tools are needed.
This unit introduces the modern methods of causal inference. You will learn Rubin’s Potential Outcomes framework, and how to use this framework to clarify what data can tell you about a causal effect of interest. You will learn various methods to estimate causal effects from observational and experimental data. Critically, you will be able to gain a deep understanding of the role that different assumptions play in determining what one can learn from data regarding causal questions. The skills you can acquire in this course are applicable to explore causal questions and undertake policy evaluation in a myriad of fields, including economics, criminology, sociology and politics, development, medicine, epidemiology or psychology, to mention but a few.
A previous course on statistical methods (e.g. ECON10072, SOST10062, CRIM20452, MATH10282).
Specifically, by completing this module, you will
- Learn to estimate causal effects and answer causal questions in a rich variety of situations ranging from experimental settings to observational data from irregular assignment mechanisms
- Understand the role played by assumptions in the identification of causal effects in different settings
- Become acquaintance with a wide range of estimation and inferential methods for causal models in design based setting (e.g. instrumental variables, regression discontinuity, difference in difference) and model based settings (panel data, matching methods) as well as some more advanced techniques (principal stratification and partial identification)
- You will learn to use the free software R to implement those statistical methods
Student should, at the end of this course, be able to:
Select, among a pool of competing methods, those most appropriate to estimate the effect of a policy, experiment or intervention on an outcome of interest.
Implement the selected estimator using widely available software such as R.
Successfully write a report explaining and supporting the findings of their analyses.
Conditional on having a clear policy or research question, students will be able to design policies, experiments and interventions to estimate causal effects.
Teaching and learning methods
Teaching will be based on asynchronous lectures, regular exercises, and a weekly live session.
Please note the information in scheduled activity hours are for guidance only and may change.
|Written assignment (inc essay)||50%|
Other: class participation (10%).
Students will receive summative feedback which gives you a mark for your assessed work.
Rosenbaum, P. (2017) Observation and Experiment: An Introduction to Causal Inference. Cambridge University Press.
Imbens, G. and Rubin, D. (2015) Causal Inference for Statistics, Social, and Biomedical Sciences, An Introduction. Cambridge University Press.
Morgan and Winship, (2015). Counterfactual and Causal Inference: Methods and Principles for Social Science Research. Cambridge University Press.
Manski, C. (2007) Identification for prediction and decision. Harvard University Press.
|Scheduled activity hours|
|Practical classes & workshops||10|
|Independent study hours|
|Eduardo Fe Rodriguez||Unit coordinator|