  Regression discontinuity designs are frequently used to estimate the causal
effect of election outcomes and policy interventions. In these contexts,
treatment effects are typically estimated with covariates included to improve
efficiency. While including covariates improves precision asymptotically, in
practice, treatment effects are estimated with a small number of observations,
resulting in considerable fluctuations in treatment effect magnitude and
precision depending upon the covariates chosen. This practice thus incentivizes
researchers to select covariates which maximize treatment effect statistical
significance rather than precision. Here, 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.
