abstract.txt

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,
$\indicator{R<c}$, 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.