Rahul Singh, Moses Stewart
arXiv 16 Jul 2025 · Econometrics
arXiv:2507.12693 · PDF · DOI · OpenAlex · Extracted main text
Standard regression discontinuity design (RDD) models rely on the continuity of expected potential outcomes at the cutoff. The standard continuity assumption can be violated by strategic manipulation of the running variable, which is realistic when the cutoff is widely known and when the treatment of interest is a social program or government benefit. In this work, we identify the treatment effect despite such a violation, by leveraging a placebo treatment and a placebo outcome. We introduce a local instrumental variable estimator. Our estimator decomposes into two terms: the standard RDD estimator of the target outcome's discontinuity, and a new adjustment term based on the placebo outcome's discontinuity. We show that our estimator is consistent, and we justify a robust bias-corrected inference procedure. Our method expands the applicability of RDD to settings with strategic behavior around the cutoff, which commonly arise in social science.
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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 | Lee, D. S. and Lemieux, T (2010) Regression discontinuity designs in economics | 0.928 | 4 | 3 | 100% |
| 2 | Calonico, S., Cattaneo, M. D., and Titiunik, R (2014) Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs | 0.843 | 5 | 3 | 60% |
| 3 | Hahn, J., Todd, P., and Van der Klaauw, W (2001) Identification and estimation of treatment effects with a regression-discontinuity design | 0.794 | 8 | 5 | 50% |
| 4 | Calonico, S., Cattaneo, M. D., Farrell, M. H., and Titiunik, R (2019) Regression Discontinuity Designs Using Covariates | 0.794 | 6 | 3 | 50% |
| 5 | Fan, J. and Gijbels, I (1992) Variable Bandwidth and Local Linear Regression Smoothers | 0.737 | 3 | 2 | 100% |
| 6 | Cattaneo, M. D., Idrobo, N., and Titiunik, R (2024) A Practical Introduction to Regression Discontinuity Designs | 0.644 | 2 | 2 | 100% |
| 7 | Imbens, G. W. and Lemieux, T (2008) Regression discontinuity designs: A guide to practice | 0.644 | 2 | 2 | 100% |
| 8 | Tchetgen Tchetgen, E. J., Ying, A., Cui, Y., Shi, X., and Miao, W (2024) An introduction to proximal causal inference | 0.644 | 2 | 2 | 100% |
| 9 | Angrist, J. D., Lavy, V., Leder-Luis, J., and Shany, A (2019) Maimonides' Rule Redux | 0.511 | 2 | 1 | 100% |
| 10 | Abdulkadiroǧlu, A., Angrist, J. D., Narita, Y., Pathak, P. A., and Z… (2017) Regression Discontinuity in Serial Dictatorship: Achievement Effects at Chicago's Exam Schools | 0.405 | 1 | 1 | 100% |
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