  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.
