标题： 基于收缩的具有许多相关处理的回归 显示英文标题

标题： Shrinkage-Based Regressions with Many Related Treatments

摘要： 在使用观察性因果模型时，从业者常常希望区分许多相关且部分重叠的处理因素的影响。 例如包括估计不同营销接触点的处理效应、订购不同类型的产品，或注册不同的服务。 那些估计单独处理系数的常用方法对于实际决策来说过于嘈杂。 我们提出了一种计算轻量的模型，该模型使用定制的岭回归在异质模型和同质模型之间进行转换：它显著降低了每个个体子处理因素的均方误差，同时使我们能够轻松重建聚合处理的因素影响。 我们在理论和模拟中展示了该估计器的特性，并说明了它如何在Wayfair实现了有针对性的决策。 显示英文摘要

摘要： When using observational causal models, practitioners often want to disentangle the effects of many related, partially-overlapping treatments. Examples include estimating treatment effects of different marketing touchpoints, ordering different types of products, or signing up for different services. Common approaches that estimate separate treatment coefficients are too noisy for practical decision-making. We propose a computationally light model that uses a customized ridge regression to move between a heterogeneous and a homogenous model: it substantially reduces MSE for the effects of each individual sub-treatment while allowing us to easily reconstruct the effects of an aggregated treatment. We demonstrate the properties of this estimator in theory and simulation, and illustrate how it has unlocked targeted decision-making at Wayfair.