# Regression Discontinuity Designs Using Covariates

arXiv: https://arxiv.org/abs/1809.03904 · HTML: https://mlsanity.com/paper/1809.03904/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2018.

## Built on by (most cited first, 6 of 6)

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- [Sample Observed Effects: Enumeration, Randomization and Generalization](https://mlsanity.com/paper/2108.04376/) (2021)
- [Hierarchical Gaussian Process Models for Regression Discontinuity/Kink under Sharp and Fuzzy Designs](https://mlsanity.com/paper/2110.00921/) (2021)
- [Inference at the data's edge: Gaussian processes for modeling and inference under model-dependency, poor overlap, and extrapolation](https://mlsanity.com/paper/2407.10442/) (2024)
- [Effect Identification and Unit Categorization in the Multi-Score Regression Discontinuity Design with Application to LED Manufacturing](https://mlsanity.com/paper/2508.15692/) (2025)

## Abstract (arXiv)

We study regression discontinuity designs when covariates are included in the estimation. We examine local polynomial estimators that include discrete or continuous covariates in an additive separable way, but without imposing any parametric restrictions on the underlying population regression functions. We recommend a covariate-adjustment approach that retains consistency under intuitive conditions, and characterize the potential for estimation and inference improvements. We also present new covariate-adjusted mean squared error expansions and robust bias-corrected inference procedures, with heteroskedasticity-consistent and cluster-robust standard errors. An empirical illustration and an extensive simulation study is presented. All methods are implemented in textttR and textttStata software packages.