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Extrapolation in Regression Discontinuity Design Using Comonotonicity

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  • Ben Deaner
  • Soonwoo Kwon

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.

Suggested Citation

  • Ben Deaner & Soonwoo Kwon, 2025. "Extrapolation in Regression Discontinuity Design Using Comonotonicity," Papers 2507.00289, arXiv.org.
    • Handle: RePEc:arx:papers:2507.00289
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    References listed on IDEAS

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    Cited by:

    1. Yuta Okamoto, 2025. "Joint Inference for the Regression Discontinuity Effect and Its External Validity," Papers 2509.26380, arXiv.org.

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