Experimental Analysis of Public Policies

A Master course on applied policy evaluation, causal inference, and econometric design. The course helps students move from a policy question to a credible empirical comparison by making the counterfactual, identifying variation, identifying assumption, and estimand explicit.

Instructor: Etienne Dagorn, Assistant Professor in Economics at LEM-U Lille.

Email: etienne.dagorn@univ-lille.fr

Website: etiennedagorn.github.io/edagorn

Current Teaching Slides

The eight methodological topics retain their numbering. The teaching decks combine related topics; the lecture guide also lists background and topic-specific slides.

Course Map

Course sequence from causal questions and counterfactuals to selection, RCTs, IV, DiD, RDD, synthetic control, heterogeneity, external validity, and replication.

Learning Objectives

Design and Identification

  • Formulate a causal policy-evaluation question.
  • Define treatment, outcome, population, time horizon, and estimand.
  • Explain the missing counterfactual and the selection problem.
  • Distinguish description, prediction, estimation, inference, and causal identification.

Methods and Interpretation

  • Assess randomised evaluations, IV, DiD, event studies, RDD, and synthetic control.
  • Interpret ATE, ATT, LATE, group-time ATT, and cutoff-local effects.
  • Use diagnostics without treating them as proof of identification.
  • Communicate empirical results to technical and non-technical audiences.

R Resources

The course R files are short starter scripts for the homework. They generate their own data, use base R for the submitted calculations, and add optional ggplot2 figures for interpretation.

Core Recommended Texts

Assessment

ComponentWeightFormat
In-class QCM quizzes (3)40%Three short in-class online quizzes.
Final written exam60%In-class written exam based on applied empirical-design scenarios.