2020/09/16 by Takuya Ishihara, Masayuki Sawada, Ishihara, Takuya +1
Computer Science · Decision Sciences · Mathematics · #Advanced Statistical Methods and Models #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning and Data Classification #Methodology (stat.ME) #Optimal Experimental Design Methods
paper · pdf · doi:10.48550/arxiv.2009.07551
openalex publication_date 2020/09/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present simple low-level conditions for identification in regression discontinuity designs using a potential outcome framework for the manipulation of the running variable. Using this framework, we replace the existing identification statement with two restrictions on manipulation. Our framework highlights the critical role of the continuous density of the running variable in identification. In particular, we establish the low-level auxiliary assumption of the diagnostic density test under which the design may detect manipulation against identification and hence is manipulation-robust.