Physics and Astronomy Building Room 1434A
Rocio Titiunik
University of Michigan

The Regression Discontinuity (RD) design is one of the most widely used non-experimental strategies for the study of treatment effects in the social, behavioral, biomedical, and statistical sciences. In this design, units are assigned a score and a treatment is offered if the value of that score exceeds a known threshold—and withheld otherwise. In this talk, I will discuss the assumptions under which the RD design can be used to learn about treatment effects, and how to make valid inferences about them based on modern theoretical results in nonparametrics that emphasize the importance of extrapolation of regression functions and misspecification biases near the RD cutoff. If time permits, I will also discuss the common approach of augmenting nonparametric regression models using predetermined covariates in RD setups, and how this affects nonparametric identification of as well as statistical inference about the RD parameter. The talk will also present a more general version of the RD design based on multiple cutoffs, which expands the generalizability of the standard RD design by allowing researchers to test richer hypotheses regarding the heterogeneity of the treatment effect and, under additional assumptions, to extrapolate the treatment effect to score values far from the cutoff.

Rocío Titiunik is the James Orin Murfin Professor of Political Science at the University of Michigan. She specializes in quantitative methodology for the social sciences, with emphasis on quasi-experimental methods for causal inference and political methodology. Her research interests lie at the intersection of political science, political economy, and applied statistics, particularly on the development and application of quantitative methods to the study of political institutions. Her recent methodological research includes the development of statistical methods for the analysis and interpretation of treatment effects and program evaluation, with emphasis on regression discontinuity (RD) designs. Her recent substantive research centers on democratic accountability and the role of party systems in developing democracies. Rocio’s work appeared in various journals in the social sciences and statistics, including the American Political Science Review, the American Journal of Political Science, the Journal of Politics, Econometrica, the Journal of the American Statistical Association, and the Journal of the Royal Statistical Society. In 2016, she received the Emerging Scholar Award from the Society for Political Methodology, which honors a young researcher who is making notable contributions to the field of political methodology. She is a member of the leadership team of the Empirical Implications of Theoretical Models (EITM) Summer Institute, member of Evidence in Governance and Politics (EGAP), and has served in various leadership roles for the American Political Science Association and for the Society for Political Methodology. She has also served as Associate Editor for Political Science Research and Methods and the American Journal of Political Science, has served in the Advisory Committee for the Social, Behavioral, and Economic Sciences Directorate and in the advisory panel for Methodology, Measurement, and Statistics program of the National Science Foundation.

Physics and Astronomy Building Room 1434A
Kosuke Imai
Harvard University

In many social science experiments, subjects often interact with each other and as a result one unit’s treatment influences the outcome of another unit. Over the last decade, a significant progress has been made towards causal inference in the presence of such interference between units. Researchers have shown that the two-stage randomization of treatment assignment enables the identification of average direct and spillover effects. However, much of the literature has assumed perfect compliance with treatment assignment. In this paper, we establish the nonparametric identification of the complier average direct and spillover effects in two-stage randomized experiments with interference and noncompliance. In particular, we consider the spillover effect of the treatment assignment on the treatment receipt as well as the spillover effect of the treatment receipt on the outcome. We propose consistent estimators and derive their randomization-based variances under the stratified interference assumption. We also prove the exact relationship between the proposed randomization-based estimators and the popular two-stage least squares estimators. Our methodology is motivated by and applied to the randomized evaluation of the India’s National Health Insurance Program (RSBY), where we find some evidence of spillover effects on both treatment receipt and outcome. The proposed methods are implemented via an open-source software package.

Kosuke Imai is a Professor in the Department of Government and the Department of Statistics at Harvard University. He is also an affiliate of the Institute for Quantitative Social Science where his primary office is located. Before moving to Harvard in 2018, Imai taught at Princeton University for 15 years where he was the founding director of the Program in Statistics and Machine Learning. He specializes in the development of statistical methods and their applications to social science research and is the author of Quantitative Social Science: An Introduction (Princeton University Press, 2017). Outside of Harvard, Imai is currently serving as the President of the Society for Political Methodology. He is also Professor of Visiting Status in the Graduate Schools of Law and Politics at The University of Tokyo.