I too have had a similar dilemma with RD designs especially in terms of estimation in SAS. I've combed the internet as well as past SAS Global Forum papers and found very little.  I understand RD designs as a quasi-experimental method in the context of Angrist and Pishke. They loosely claim that even a basic regression with a dummy intercept capturing the discontinuity - see more details here on my interpretation of this) will do a pretty good job capturing the treatment effect. In terms of estimation in SAS, I have compared results from the RD function in R (which allows for more complicated local linear regression techniques and bandwitdth selection similar to STATA) and compared the results to an estimation in SAS using PROC GLM or GLIMMIX with interactions and obtained very similar estimation of the treatment effect. I also try to keep most of my analysis in SAS for production analytics and interoperability with other analysts.  Again, I'm thinking of RD as a quasi-experimental method that identifies the treatment effect using the quasi-experimental variation generated by near random treatment assignment near the cutoff  Xo.  Your description does seem like an analysis of a discontinuity, but not exactly in a context I'm familiar with. Could your treatment effect also be estimated using other methods that exploit discontinuities such as a difference-in-difference approach or interrupted time series?  I also have had the same issue with DD as RD in terms of good references doing the estimation in SAS. Interrupted time series is much more proliferate in terms of examples and documentation. I'm very new with these methods so I'm interested in learning more from responses that hopefully will follow. ... View more