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The Regression Discontinuity Design: Methods and Applications
November 5, 2018 @ 4:00 pm - 5:00 pm
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. 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. If time permits, I will also discuss 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 appears 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-at-large of the Society for Poltical Methodology, and member of Evidence in Governance and Politics (EGAP). She is also an Associate Editor for Political Science Research and Methods and the American Journal of Political Science, and has served in the advisory panel for the Methodology, Measurement, and Statistics program of the National Science Foundation.