A nonparametric Bayesian methodology for regression discontinuity designs
A nonparametric Bayesian methodology for regression discontinuity designs
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DOI：
10.1016/j.jspi.2019.01.003
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发表时间：
2019-09-01
影响因子：
0.9
通讯作者：
Miratrix, Luke W.
中科院分区：
文献类型：
--
作者：
Branson, Zach;Rischard, Maxime;Miratrix, Luke W.
在回归间断设计中，最流行的估计阈值处理效果的方法之一是局部线性回归(LLR)，它对接近阈值的单位赋予更大的权重。我们提出了一种高斯过程回归方法，作为回归不连续设计的贝叶斯模拟。我们的方法通过对平均反应函数设置一般的先验来灵活地适应治疗和控制反应。此外，与LLR不同，我们的方法可以在估计治疗效果时纳入如何加权单元的不确定性。我们证明了我们的方法在估计阈值下的平均治疗效果方面是一致的。此外，我们通过仿真发现，与标准LLR和最新的LLR方法相比，我们的方法具有良好的覆盖、间隔长度和均方误差特性。最后，我们通过研究首轮选秀权对NBA篮球运动员的表现和上场时间的影响，探讨了我们的方法在真实世界的例子中的表现。(C)2019爱思唯尔B.V.保留所有权利。
One of the most popular methodologies for estimating the average treatment effect at the threshold in a regression discontinuity design is local linear regression (LLR), which places larger weight on units closer to the threshold. We propose a Gaussian process regression methodology that acts as a Bayesian analog to LLR for regression discontinuity designs. Our methodology provides a flexible fit for treatment and control responses by placing a general prior on the mean response functions. Furthermore, unlike LLR, our methodology can incorporate uncertainty in how units are weighted when estimating the treatment effect. We prove our method is consistent in estimating the average treatment effect at the threshold. Furthermore, we find via simulation that our method exhibits promising coverage, interval length, and mean squared error properties compared to standard LLR and state-of-the-art LLR methodologies. Finally, we explore the performance of our method on a real-world example by studying the impact of being a first-round draft pick on the performance and playing time of basketball players in the National Basketball Association. (C) 2019 Elsevier B.V. All rights reserved.