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Extrapolation in Regression Discontinuity Design Using Comonotonicity

Ben Deaner, Soonwoo Kwon

arXiv 30 Jun 2025 · Econometrics

arXiv:2507.00289 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We present a novel approach for extrapolating causal effects away from the margin between treatment and non-treatment in sharp regression discontinuity designs with multiple covariates. Our methods apply both to settings in which treatment is a function of multiple observables and settings in which treatment is determined based on a single running variable. Our key identifying assumption is that conditional average treated and untreated potential outcomes are comonotonic: covariate values associated with higher average untreated potential outcomes are also associated with higher average treated potential outcomes. We provide an estimation method based on local linear regression. Our estimands are weighted average causal effects, even if comonotonicity fails. We apply our methods to evaluate counterfactual mandatory summer school policies.

Citation extraction

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Angrist, Joshua D., & Rokkanen, Miikka (2015) Wanna Get Away? Regression Discontinuity Estimation of Exam School Effects Away From the Cutoff0.6936250%
2Alan, Sule, Boneva, Teodora, & Ertac, Seda (2019) Ever Failed, Try Again, Succeed Better: Results from a Randomized Educational Intervention on Grit*0.6445240%
3Mammen, Enno, Rothe, Christoph, & Schienle, Melanie (2012) Nonparametric regression with nonparametrically generated covariates0.6443267%
4Imbens, Guido, & Wager, Stefan (2019) Optimized Regression Discontinuity Designs0.64422100%
5Matsudaira, Jordan D (2008) Mandatory summer school and student achievement0.64422100%
6Schmeidler, David (1989) Subjective Probability and Expected Utility without Additivity0.64422100%
7Imbens, Guido, & Newey, Whitney (2009) Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity0.5114225%
8D'Haultfœuille, Xavier, & Février, Philippe (2015) Identification of Nonseparable Triangular Models With Discrete Instruments0.5113233%
9Torgovitsky, Alexander (2015) Identification of Nonseparable Models Using Instruments With Small Support0.5113233%
10Chernozhukov, Victor, & Hansen, Christian (2005) An IV Model of Quantile Treatment Effects0.5112250%

Showing the top 10 of 33 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Joint Inference for the Regression Discontinuity Effect and Its External Validity0.73732
2Statistical Decisions and Partial Identification: With Application to Boundary Discontinuity Design0.51121
3On Extrapolation of Treatment Effects in Multiple-Cutoff Regression Discontinuity Designs0.40511