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arXiv · 2509.26380

Joint Inference for the Regression Discontinuity Effect and Its External Validity

Yuta Okamoto
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Abstract

The external validity of regression discontinuity designs is crucial for informing policy but is rarely examined in applied work. To advance empirical practice, we propose a joint inference procedure for the treatment effect and its local external validity, captured by the treatment effect derivative (TED), within a robust bias correction framework. We further introduce a locally linear treatment effects assumption, which extends the scope of the TED and enables identification and the construction of a uniform confidence band for extrapolated effects. These methods apply to most empirical studies. Empirical illustrations demonstrate their practical usefulness.

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Yuta Okamoto. 2025-09-30. Joint Inference for the Regression Discontinuity Effect and Its External Validity. https://arxiv.org/abs/2509.26380

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