Before revision:  In this paper , I propose a principled , efficiency-maximizing approach for covariate adjustment of LATE in RDDs . This approach allows researchers to combine context-specific, substantive insights with automated model selection via a novel adaptive lasso algorithm. When combined with currently existing robust estimation methods, this approach improves the efficiency of LATE RDD with pre-treatment covariates . The approach will be implemented in a forthcoming R package , AdaptiveRDD which can be used to estimate and compare treatment effects generated by this approach with extant approaches .
After revision:  In this paper , I propose a principled approach for estimating RDDs which provides a means of improving precision with covariates while minimizing adverse incentives. This is accomplished by integrating the adaptive LASSO, a machine learning method, into RDD estimation using an R package developed for this purpose, adaptiveRDD. Using simulations, I show that this method significantly improves treatment effect precision, particularly when estimating treatment effects with fewer than 200 observations .