Noise-induced randomization in regression discontinuity designs
Authors
Research Topics
Paper Information
-
Journal:
Biometrika -
DOI:
10.1093/biomet/asaf003 -
Published:
March 16, 2025 -
Added to Tracker:
Feb 10, 2026
Abstract
Summary Regression discontinuity designs assess causal effects in settings where treatment is determined by whether an observed running variable crosses a prespecified threshold. Here, we propose a new approach to identification, estimation and inference in regression discontinuity designs that uses knowledge about exogenous noise (e.g., measurement error) in the running variable. In our strategy, we weight treated and control units to balance a latent variable, of which the running variable is a noisy measure. Our approach is driven by effective randomization provided by the noise in the running variable, and complements standard formal analyses that appeal to continuity arguments while ignoring the stochastic nature of the assignment mechanism.
Author Details
Dean Eckles
AuthorNikolaos Ignatiadis
AuthorStefan Wager
AuthorHan Wu
AuthorResearch Topics & Keywords
Machine Learning
Research AreaExperimental Design
Research AreaCitation Information
APA Format
Dean Eckles
,
Nikolaos Ignatiadis
,
Stefan Wager
&
Han Wu
(2025)
.
Noise-induced randomization in regression discontinuity designs.
Biometrika
, 10.1093/biomet/asaf003.
BibTeX Format
@article{paper886,
title = { Noise-induced randomization in regression discontinuity designs },
author = {
Dean Eckles
and Nikolaos Ignatiadis
and Stefan Wager
and Han Wu
},
journal = { Biometrika },
year = { 2025 },
doi = { 10.1093/biomet/asaf003 },
url = { https://doi.org/10.1093/biomet/asaf003 }
}