Introductory description

This module aims to provide doctoral students with an understanding of, and skills in applying, quantitative methods for causal inference and policy evaluation.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

1. Measuring Treatment Effects and randomised controlled experiments.
2. Local Average Treatment Effects and Instrumental Variables
3. Differences in differences and Panel Data regressions.
4. Regression Discontinuity Designs: Sharp and Fuzzy designs.
5. Synthetic Controls.
6. Propensity Score Matching
7. Coarsened Exact Matching
8. Quantile treatment effects
9. Structural Equation Modelling (multivariate analysis).
10. Presentation of the Empirical Project

Learning outcomes

By the end of the module, students should be able to:

• Have an in-depth understanding of assumptions underlying causal analyses with experimental and observational data.
• Have an in-depth knowledge of the presented methods to draw causal inferences

Indicative reading list

Reading lists can be found in Talis

Interdisciplinary

Elements of Economics, Statistics, Management, and Political Science.

Subject specific skills

Design an identification strategy adequate to the empirical research question and data at hand.
Implement the identification strategy using the adequate estimator and statistical software.
Evaluate empirical results achieved by applying causal inference methods.
An in-depth understanding of assumptions underlying causal analyses with experimental and observational data.
An in-depth knowledge of the presented methods to draw causal inferences.

Transferable skills

Problem solving abilities
Communication skills.
Analytical skills.
Confidence as user of statistical software.