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KAFP 3220 - Evaluation of Public Policy

Type d'enseignement : Lecture alone

Semester : Spring 2017-2018

Number of hours : 24

Language of tuition : English


Students must have previously taken the course on “Statistics and Data Analysis for Policymakers” taught in the first semester. The course is sometimes based on articles from academic journals and provides Stata codes that facilitate implementation of evaluation methods which are introduced. Students will not be expected to understand thoroughly all the mathematics or statistics. Instead, our focus will be on clarifying the main concepts, assumptions, and methods used in evaluation studies.

Course Description

This course aims to provide students with a range of specific skills that will enable them to undertake impact evaluation of public policy. Too often policy is implemented but not fully evaluated. Without evaluation we cannot then tell what the short or longer term impact of a particular policy has been. On this course, students will learn the statistical skills needed to evaluate public policies. They will also have the opportunity to read representative articles that are published in top-ranked academic journals and that make use of the techniques that are presented in the course. A particular feature of this course is that it provides these statistical skills in a real world context of policy evaluation. It also focuses primarily not on experimental evaluation (Random Control Trials) but rather quasi-experimental methodologies that can be used where an experiment is not desirable or feasible. Implementation of these methods will be illustrated by several Stata codes.


  • FOUGERE, Denis (Research Director CNRS)
  • HEIM, Arthur (Chef de Projets)

Pedagogical format


Course validation

A mid-term exam (written test) 35% of final mark. A final exam (written test) 65% of final mark.


Before each class, students should read book chapters, articles and working papers quoted in the reading list corresponding to the lecture (the reading list will be distributed to students during the first class).

Required reading

  • Guido Imbens and Donald Rubin: Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press, 2015, 644 pages
  • Joshua Angrist and Jorn-Stefen Pischke: Mastering 'Metrics - The Path from Cause to Effect. Princeton University Press, 2015, 352 pages
  • William Holmes: Using Propensity Scores in Quasi-Experimental Designs. Sage Publications, 2013, 360 pages
  • Rachel Glennerster and Kudzai Takavarasha: Running Randomized Experiments – A Practical Guide. Princeton University Press, 2013, 480 pages
  • Stephen Morgan and Christopher Winship: Counterfactuals and Causal Inference: Methods and Principles for Social Research. Cambridge University Press, 2007, 328 pages