Quantitative Public Policy Analysis II   

Prof. Cautrès is very passionate about teaching. He created an extremely meaningful learning experience, relating and applying methods to practical issues. I have never learned statistics this well before. — participant from Japan

Whether you think they should or they should not, numbers, data, and quantitative methods matter to today's public policy and policy analysis. Policymakers and administrators alike use numbers to support their (normative) arguments on what policies should be implemented, whether governments should or should not provide certain services, or whether it is the right time to engage in policy reform. At the same time, policy analysts use data and wide variety of quantitative methods to predict and evaluate the success or failure of new policies and to engage in evidence-based research on the impact of past policy interventions.

This course is the second part in a two-course sequence (cf. Quantitative Public Policy I). It is designed to provide participants with advanced statistical skills that allow them not only to engage with quantitative policy reports and publications, but to actively use advanced quantitative methods to analyze public policies as part of their academic, public sector consulting, or civil service careers.


This one-week, 20-hour course runs Monday-Friday, 9:00 am-1:00 pm, June 26-30, 2017.


Bruno Cautrès (picture), Sciences Po Paris

Detailed Description

Building on the material covered by the first course in the two-course Quantitative Public Policy Analysis sequence (cf. Quantitative Public Policy I), this course moves from basic statistical concepts and regression-based reasoning to more advanced tools and provides a survey of quantitative methods for empirical studies and public policy research. The focus is on statistical methods for causal inference, i.e., methods designed to address questions that concern the causal link and the impact of causes on outcomes, where causes can be a policy intervention or a change in political institutions or economic conditions and an outcome might be (changes in) household income, public support, election results, crime rates, etc.

The course starts out with a discussion of the strengths and limitations of multiple regression analysis and the relationship between regression and causal modeling before covering a variety of quasi-experiment designs for causal inference. Participants will learn about a number of extensions and alternatives to the standard linear regression model that are commonly employed in today’s advanced public policy analyses, among them:

  • Panel data methods (fixed and random effects, difference-in-differences)

  • Instrumental variable estimation

  • Regression discontinuity designs

  • Quantile regression

  • Limited dependent variables techniques (binary logit, ordinal logit analysis)

In addition to introducing participants to these advanced methods, the course also analyzes their strengths and weaknesses of these methods and provides participants with hands-on experience on how to apply statistical techniques to real-world data. Applications are drawn from public policy as well as various social science disciplines, such as economics, political science, and sociology.


We strongly encourage participants to combine this course with the introductory Quantitative Public Policy Analysis I or the complementary Experimental Methods course. Alternatively, participants should be familiar with the material covered by Quantitative Public Policy Analysis I to guarantee that they get the most out of this course. Experience with the statistical software Stata is helpful, but not required.


Participants are expected to bring a WiFi-enabled laptop computer. Access to data, temporary licenses for the course software, and installation support will be provided by the Methods School.

Core Readings

Will be provided.

Suggested Readings

Agresti, Alan, and Barbara Finlay. 2008. Statistical Methods for the Social Sciences. 4th edition. Upper Saddle River, NJ: Prentice-Hall.

Angrist, Joshua D., and Jörn-Steffen Pischke. 2009. Mostly Harmless Econometrics. An Empiricist's Companion. Princeton, NJ: Princeton University Press.

Register Now