One of the most popular methodologies for estimating the average treatmenteffect at the threshold in a regression discontinuity design is local linearregression (LLR), which places larger weight on units closer to the threshold.We propose a Gaussian process regression method that acts as a Bayesian analogto LLR for sharp regression discontinuity designs. Our Gaussian processregression method provides a flexible fit for treatment and control responsesby placing a general prior on the mean response functions. We prove our methodis consistent in estimating the average treatment effect at the threshold.Furthermore, we find via simulation that our method exhibits promisingcoverage, interval length, and mean squared error properties compared tostandard LLR and state-of-the-art LLR methodologies. Finally, we explore theperformance of our method on a real-world example by studying the impact ofbeing a first-round draft pick on the performance and playing time ofbasketball players in the National Basketball Association.
展开▼
