Estimation and Inference in Boundary Discontinuity Designs
主讲人：Matias D. Cattaneo（Princeton）
主持老师：（北大经院）王一鸣、巩爱博
参与老师：（北大经院）刘蕴霆、王法、王熙
时间：2025年5月30日（周五） 10:00-11:30
地点（线下）：北京大学经济学院606会议室
报告摘要：
Boundary Discontinuity Designs are used to learn about treatment effects along a continuous boundary that splits units into control and treatment groups according to a bivariate score variable. These research designs are also called Multi-Score Regression Discontinuity Designs, a leading special case being Geographic Regression Discontinuity Designs. We study the statistical properties of commonly used local polynomial treatment effects estimators along the continuous treatment assignment boundary. We consider two distinct approaches: one based explicitly on the bivariate score variable for each unit, and the other based on their univariate distance to the boundary. For each approach, we present pointwise and uniform estimation and inference methods for the treatment effect function over the assignment boundary. Notably, we show that methods based on univariate distance to the boundary exhibit an irreducible large misspecification bias when the assignment boundary has kinks or other irregularities, making the distance-based approach unsuitable for empirical work in those settings. In contrast, methods based on the bivariate score variable do not suffer from that drawback. We illustrate our methods with an empirical application. Companion general-purpose software is provided.
主讲人简介：
Matias D. Cattaneo is a Professor of Operations Research and Financial Engineering (ORFE) at Princeton University, where 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). His research spans econometrics, statistics, data science, and decision science, with applications to program evaluation and causal inference. His work is interdisciplinary, and often motivated by quantitative problems in the social, behavioral, and biomedical sciences. His research often integrates nonparametric, semiparametric, high-dimensional, and machine learning methods to develop robust estimation and inference techniques.Matias is an elected Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the International Association for Applied Econometrics. He serves on the editorial boards of leading journals, including Journal of the American Statistical Association, Econometrica, Operations Research, Statistical Science, the Econometrics Journal, the Journal of Econometrics, Econometric Theory, and the Journal of Causal Inference. He is also an Amazon Scholar and has advised numerous governmental, multilateral, non-profit, and private organizations around the world.
