CARE Seminar: Matias Cattaneo, Princeton

Speaker

Matias Cattaneo, Princeton

Biography

Matias D. Cattaneo is a Professor of Operations Research and Financial Engineering (ORFE) at Princeton University. He is also an Associated Faculty in the School of Public and International Affairs (SPIA), the Department of Economics, and the Program in Latin American Studies (PLAS), and an Affiliated Faculty in the Data-Driven Social Science (DDSS) initiative, the AI at Princeton initiative, and the Center for Statistics and Machine Learning (CSML). Beyond academia, he serves as an Amazon Scholar, and has advised a wide range of organizations worldwide.

Matias is an elected Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the International Association for Applied Econometrics, and an elected Member of the International Statistical Institute. His research interests are interdisciplinary, motivated by quantitative challenges arising in the social, behavioral, and biomedical sciences. He integrates econometrics, statistics, applied mathematics, data science, and decision science, with applications to program evaluation and causal inference.

Matias earned his Ph.D. in Economics (2008) and M.A. in Statistics (2005) from the University of California, Berkeley, a Master in Economics from Universidad Torcuato Di Tella (2003), and a Licentiate in Economics from Universidad de Buenos Aires (2000). Originally from Buenos Aires, Argentina, Matias is married to Rocio Titiunik, and they have two daughters.

Title

"Boundary Discontinuity Designs: Theory and Practice" (joint work with Rocio Titiunik and Rae Yu)

Abstract

Abstract Ruiqi (Rae) Yu§ We review the literature on boundary discontinuity (BD) designs, a powerful nonexperimental research methodology that identifies causal effects by exploiting a thresholding treatment assignment rule based on a bivariate score and a boundary curve. This methodology generalizes standard regression discontinuity designs based on a univariate score and scalar cutoff, and has specific challenges and features related to its multi-dimensional nature. We synthesize the empirical literature by systematically reviewing over 80 empirical papers, tracing the method’s application from its formative uses to its implementation in modern research. In addition to the empirical survey, we overview the latest methodological results on identification, estimation and inference for the analysis of BD designs, and offer recommendations for practice. Keywords: regression discontinuity, treatment effects estimation, causal inference.