Noise-induced randomization in regression discontinuity designs

• 成果类型: Article
• 署名作者: Eckles, Dean; Ignatiadis, Nikolaos; Wager, Stefan; Wu, Han
• 署名单位: Massachusetts Institute of Technology (MIT); University of Chicago; University of Chicago; Stanford University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asaf003
• 发表日期: 2025
• 关键词: confidence-intervals; causal-inference; propensity score; selection; weather; COUNT; RISK; cd4

摘要

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.