arXiv 30 Sep 2025 · Econometrics
arXiv:2509.26380 · PDF · Extracted main text
The external validity of regression discontinuity (RD) designs is essential for informing policy and remains an active research area in econometrics and statistics. However, we document that only a limited number of empirical studies explicitly address the external validity of standard RD effects. To advance empirical practice, we propose a simple joint inference procedure for the RD effect and its local external validity, building on Calonico, Cattaneo, and Titiunik (2014, Econometrica) and Dong and Lewbel (2015, Review of Economics and Statistics). We further introduce a locally linear treatment effects assumption, which enhances the interpretability of the treatment effect derivative proposed by Dong and Lewbel. Under this assumption, we establish identification and derive a uniform confidence band for the extrapolated treatment effects. Our approaches require no additional covariates or design features, making them applicable to virtually all RD settings and thereby enhancing the policy relevance of many empirical RD studies. The usefulness of the method is demonstrated through an empirical application, highlighting its complementarity to existing approaches.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Dong, Yingying and Lewbel, Arthur (2015) Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models | 1.000 | 26 | 6 | 100% |
| 2 | Calonico, Sebastian and Cattaneo, Matias D and Titiunik, Rocio (2014) Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs | 1.000 | 24 | 5 | 100% |
| 3 | Matias D. Cattaneo and Luke Keele and Rocío Titiunik and Gonzalo Vaz… (2021) Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs | 1.000 | 12 | 5 | 100% |
| 4 | Yuta Okamoto and Yuuki Ozaki (2025) On Extrapolation of Treatment Effects in Multiple-Cutoff Regression Discontinuity Designs self | 1.000 | 7 | 3 | 100% |
| 5 | Joshua D. Angrist and Miikka Rokkanen (2015) Wanna Get Away? Regression Discontinuity Estimation of Exam School Effects Away From the Cutoff | 1.000 | 5 | 3 | 100% |
| 6 | Marinho Bertanha and Guido W. Imbens (2020) External Validity in Fuzzy Regression Discontinuity Designs | 1.000 | 5 | 3 | 100% |
| 7 | Ben Deaner and Soonwoo Kwon (2025) Extrapolation in Regression Discontinuity Design Using Comonotonicity | 0.737 | 3 | 2 | 100% |
| 8 | Marinho Bertanha (2020) Regression Discontinuity Design with Many Thresholds | 0.644 | 2 | 2 | 100% |
| 9 | Hahn, Jinyong and Todd, Petra and Van der Klaauw, Wilbert (2001) Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design | 0.644 | 2 | 2 | 100% |
| 10 | Calonico, Sebastian and Cattaneo, Matias D and Farrell, Max H (2020) Optimal Bandwidth Choice for Robust Bias-Corrected Inference in Regression Discontinuity Designs | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 45 scored citations.