# Analysis of regression discontinuity designs using censored data

arXiv: https://arxiv.org/abs/1908.03646 · HTML: https://mlsanity.com/paper/1908.03646/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [Nonparametric Regression Discontinuity Designs with Survival Outcomes](https://mlsanity.com/paper/2604.03502/) (2026)

## Abstract (arXiv)

In medical settings, treatment assignment may be determined by a clinically important covariate that predicts patients' risk of event. There is a class of methods from the social science literature known as regression discontinuity (RD) designs that can be used to estimate the treatment effect in this situation. Under certain assumptions, such an estimand enjoys a causal interpretation. However, few authors have discussed the use of RD for censored data. In this paper, we show how to estimate causal effects under the regression discontinuity design for censored data. The proposed estimation procedure employs a class of censoring unbiased transformations that includes inverse probability censored weighting and doubly robust transformation schemes. Simulation studies demonstrate the utility of the proposed methodology.