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This subject aims to familiarize students with the tools commonly used for identifying and estimating tretment effects and evaluating programs and policies. Topics to be covered include the use and interpretation of Difference in Difference, Instrumental Variables, and Regression Discontinuity Design Estimators. The main theoretical ideas are illustrated with examples drawn from recent applications in the literature. Computer software such as Stata will be used.
Intended learning outcomes
On completion of this subject students will be able to:
- identify the problem of causal treatment effects using observational data;
- identify the circumstances under which randomized control trials can be used to overcome the problem of identification of causal treatment effects;
- evaluate the assumptions under which the microeconometric estimators studied are able to identify causal treatment effects;
- evaluate the assumptions underlying the identification strategy for each estimator studied including their benefits and limitations;
- be able to choose amongst alternative estimators of causal treatment effects and apply them to the analysis of real world data;
- know how to interpret the results from estimation using the various microeconometric estimators studied and to derive appropriate conclusions regarding the treatment effect of a policy;
- be able to critically evaluate program evaluations and be able to effectively present and discuss research findings.
- High level of development: problem solving; statistical reasoning; interpretation and analysis; use of computer software; accessing data and other information from a range of sources.
- Moderate level of development: written communication; application of theory to practice; critical thinking; synthesis of data and other information; evaluation of data and other information; receptiveness to alternative ideas.
- Some level of development: oral communication; collaborative learning; team work.
Last updated: 29 July 2022