The regression discontinuity design (RDD) has become the "gold standard" for causal inference with observational data. Local average treatment effects (LATE) for RDDs are often estimated using local linear regressions with pre-treatment covariates typically added to increase the efficiencyof treatment effect estimates, but their inclusion can have large impacts on LATE point estimates and standard errors, particularly in small samples. 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 .
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 .
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The regression discontinuity design (RDD) has become the "gold standard" for causal inference with observational data. Local average treatment effects (LATE) for RDDs are often estimated using local linear regressions with pre-treatment covariates typically added to increase the efficiencyof treatment effect estimates, but their inclusion can have large impacts on LATE point estimates and standard errors, particularly in small samples. In this paper
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
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, 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
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