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K2SP 3245 - Evaluation of Public Policy

Type d'enseignement : Lecture alone

Semester : Spring 2020-2021

Number of hours : 40

Language of tuition : English


Students must have previously taken courses on “Public Economics” (KAFP 3355) and “Policy Analysis and Policy Evaluation” (KAFP3435) taught in the first semester. The course is sometimes based on articles from academic journals that facilitate understanding 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 feature of this course is that it provides these statistical skills in a real-world context of policy evaluation. It both focuses on experimental evaluation (Random Control Trials) and quasi-experimental methodologies that can be used when an experiment is not desirable or feasible.


  • FOUGERE, Denis (Research Director CNRS)
  • HEIM, Arthur (Chefs de projets)

Pedagogical format


Course validation

A mid-term exam (written test) 1/3 A final exam (written test) 2/3


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
  • Markus Frölich and Stefan Sperlich: Impact evaluation – Treatment effects and Causal Analysis. Cambridge University Press, 2019, 417 pages.
  • Paul R. Rosenbaum: Observation & Experiment – An Introduction to Causal Inference. Harvard University Press, 2017, 374 pages
  • Paul J. Gertler, Sebastian Martinez, Patrick Premand, Laura B. Rawlings, Christel M. J. Vermeersch: Impact evaluation in practice. World Bank Publications. 2010, 264 pages.

Additional required reading

  • 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