# Bias-Aware Inference in Fuzzy Regression Discontinuity Designs

arXiv: https://arxiv.org/abs/1906.04631 · HTML: https://mlsanity.com/paper/1906.04631/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Statistical Inference under Performativity](https://mlsanity.com/paper/2505.18493/) (2025)
- [Nonparametric Regression Discontinuity Designs with Survival Outcomes](https://mlsanity.com/paper/2604.03502/) (2026)

## Abstract (arXiv)

We propose new confidence sets (CSs) for the regression discontinuity parameter in fuzzy designs. Our CSs are based on local linear regression, and are bias-aware, in the sense that they take possible bias explicitly into account. Their construction shares similarities with that of Anderson-Rubin CSs in exactly identified instrumental variable models, and thereby avoids issues with "delta method" approximations that underlie most commonly used existing inference methods for fuzzy regression discontinuity analysis. Our CSs are asymptotically equivalent to existing procedures in canonical settings with strong identification and a continuous running variable. However, due to their particular construction they are also valid under a wide range of empirically relevant conditions in which existing methods can fail, such as setups with discrete running variables, donut designs, and weak identification.