Regression Discontinuity Designs With a Continuous Treatment

Yingying Dong, Ying-Ying Lee and Michael Gou

Journal of the American Statistical Association, 2023, vol. 118, issue 541, 208-221

Abstract: The standard regression discontinuity (RD) design deals with a binary treatment. Many empirical applications of RD designs involve continuous treatments. This article establishes identification and robust bias-corrected inference for such RD designs. Causal identification is achieved by using any changes in the distribution of the continuous treatment at the RD threshold (including the usual mean change as a special case). We discuss a double-robust identification approach and propose an estimand that incorporates the standard fuzzy RD estimand as a special case. Applying the proposed approach, we estimate the impacts of bank capital on bank failure in the pre-Great Depression era in the United States. Our RD design takes advantage of the minimum capital requirements, which change discontinuously with town size.

Date: 2023
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DOI: 10.1080/01621459.2021.1923509

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