Many universities place underperforming students on "Academic Probation" (AP), granting them support and threatening suspension if performance does not improve. In a study of the effect of AP on subsequent grade point averages (GPAs), Lindo, et al. (2010) noted that AP follows a regression discontinuity design (RDD): students whose first year GPAs (the "running variable") fall below a cutoff are put on AP. However, the standard RDD procedure suffered in their dataset from a number of problems, including a discrete running variable and a McCrary test failure. This paper presents a new approach to modeling RDDs, which solves these problems. Its idea is to disentangle the outcome from the running variable, producing a transformed version of the outcome; to model the transformed outcome as independent of treatment assignment; and to estimate treatment effects using permutation inference. Whereas standard RDD analysis relies on limits of outcomes' conditional expectation functions and assumes a continuous running variable, we model the transformed data as if they emerged from a randomized experiment. Our approach estimates effects for a specific sample of subjects, is robust to small samples and discrete running variables, and is amenable to solutions to a McCrary test failure.