On Extrapolation of Treatment Effects in Multiple-Cutoff Regression Discontinuity Designs

Yuta Okamoto and Yuuki Ozaki
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Yuta Okamoto: Graduate School of Economics, Hitotsubashi University, JAPAN and Junior Research Fellow, Research Institute for Economics and Business Administration, Kobe University, JAPAN
Yuuki Ozaki: Attax Co., Ltd., JAPAN

No DP2026-20, Discussion Paper Series from Research Institute for Economics & Business Administration, Kobe University

Abstract: This paper investigates how to learn treatment effects away from the cutoff point in multiple-cutoff regression discontinuity designs. Using a microeconomic model, we demonstrate that the parallel-trend type assumption proposed in the literature is justified when cutoff positions are assigned as if randomly and the running variable is non-manipulable (e.g., parental income). However, when the running variable is partially manipulable (e.g., test scores), extrapolations based on that assumption can be biased. As a complementary strategy, we propose a novel partial identification approach based on empirically motivated assumptions. We also develop a uniform inference procedure and provide two empirical illustrations.

Keywords: Decision model; External validity; Partial identification; Regression discontinuity designs (search for similar items in EconPapers)
JEL-codes: C14 C21 D00 D84 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2026-06
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