Limitless Regression Discontinuity: Causal Inference for a Population Surrounding a Threshold
Year: 2014
Type: preprint
Abstract: Conventionally, regression discontinuity analysis contrasts a univariate regression's limits as its independent variable, $R$, approaches a cut-point, $c$, from either side. Alternative methods target the average treatment effect in a small region around $c$, at the cost of an assumption that treatment assignment, $\mathcal{I}\left[R<c\right]$, is ignorable vis a vis potential outcomes. Instead, the method presented in this paper assumes Residual Ignorability, ignorability of treatment assignment vis a vis detrended potential outcomes. Detrending is effected not with ordinary least squares but with MM-estimation, following a distinct phase of sample decontamination. The method's inferences acknowledge uncertainty in both of these adjustments, despite its applicability whether $R$ is discrete or continuous; it is uniquely robust to leading validity threats facing regression discontinuity designs.
Source: arXiv (Cornell University)
Authors: Adam Sales, Ben B. Hansen
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3036447771