Quantitative Public Policy Analysis I   

Professor Cautrès is an excellent teacher, extremely good at explaining even the most difficult concepts, and his enthusiasm is contagious. — participant from India

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 first part in a two-course sequence (cf. Quantitative Public Policy II). It is designed to provide participants with the basic skills needed to engage with quantitative policy reports and publications, familiarizes them with fundamental quantitative methods, and teaches them how to use some of the statistical tools required for a successful career in today's public policy world.


This one-week, 17.5-hour course runs Monday-Friday, 9:00 am-12:30 pm, July 1-5, 2019.


Bruno Cautrès (picture), Sciences Po Paris

Detailed Description

This first course in the two-course Quantitative Public Policy Analysis sequence (cf. Quantitative Public Policy II) introduces participants to the major concepts and tools used by public policy specialists for causal reasoning and the quantitative evaluation of policies. Among the concepts covered by the course are counterfactuals, potential outcomes, quasi-experiments, treatment effects, and before-after effects. Participants also learn how statistical methods can be used to develop a formal framework for quasi-experimental reasoning and to address major public policy questions related to such diverse issues as education, public health, social policies, business regulation, etc.

Where the first part of the course focuses on the role of quantitative methods in public policy analysis and evaluation and theoretical considerations behind such issues as causality and the Rubin causal model, experiments and quasi-experiments, and internal validity, the second part is both theoretical and applied.

Participants will learn the basics of the classic linear regression model and how to use regression to test the effect of such 'treatments' as a change in policy or exposure to political reform. We study a variety of techniques within the multiple regression framework – dummy variables, interaction effects, Chow test for subgroups and structural breaks – that allows us to use regression for group comparisons. By the end of the course, participants will not only have a clear understanding of the relationship between the comparison of treatment and control groups and regression, but to conduct their own, basic quantitative analyses of social, political, or economic data and to evaluate public policies with the help of the popular statistical software Stata.


There are no formal prerequisites for this course. Basic knowledge of descriptive statistics and a background in the use of the statistical software Stata would be helpful (cf. Applied Data Analysis), but are 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.

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