Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs

• 成果类型: Article
• 署名作者: Cattaneo, Matias D.; Keele, Luke; Titiunik, Rocio; Vazquez-Bare, Gonzalo
• 署名单位: Princeton University; University of Pennsylvania; Princeton University; University of California System; University of California Santa Barbara
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1751646
• 发表日期: 2021
• 页码: 1941-1952
• 关键词: university grants; inference

摘要

In nonexperimental settings, the regression discontinuity (RD) design is one of the most credible identification strategies for program evaluation and causal inference. However, RD treatment effect estimands are necessarily local, making statistical methods for the extrapolation of these effects a key area for development. We introduce a new method for extrapolation of RD effects that relies on the presence of multiple cutoffs, and is therefore design-based. Our approach employs an easy-to-interpret identifying assumption that mimics the idea of common trends in difference-in-differences designs. We illustrate our methods with data on a subsidized loan program on post-education attendance in Colombia, and offer new evidence on program effects for students with test scores away from the cutoff that determined program eligibility. Supplementary materials for this article are available online.