Matching on covariates is a well-established framework for estimating causal effects in observational studies. The principal challenge in these settings stems from the often high-dimensional structure of the problem. Many methods have been introduced to deal with this challenge, with different advantages and drawbacks in computational and statistical performance and interpretability. Moreover, the methodological focus has been on matching two samples in binary treatment scenarios, but a dedicated method that can optimally balance samples across multiple treatments has so far been unavailable. This article introduces a natural optimal matching method based on entropy-regularized multimarginal optimal transport that possesses many useful properties to address these challenges. It provides interpretable weights of matched individuals that converge at the parametric rate to the optimal weights in the population, can be efficiently implemented via the classical iterative proportional fitting procedure, and can even match several treatment arms simultaneously. It also possesses demonstrably excellent finite sample properties.
翻译:共变法是估算观察研究中因果效果的既定框架。这些环境中的主要挑战来自问题通常的高维结构。已经采用了许多方法来应对这一挑战,在计算和统计绩效和可解释性方面有不同的优缺点。此外,方法上的重点是在二进制处理情景中匹配两个样本,但迄今还没有一种能够最佳平衡多个处理方法样本的专门方法。这一条引入了一种自然最佳匹配方法,其基础是具有许多有用特性的环球常规多边际最佳运输,以应对这些挑战。它提供了符合对称率的人的可解释权重,与人口的最佳权重相匹配,可以通过传统的迭式比例搭配程序高效实施,甚至可以同时匹配几种处理武器。它还具有明显的精细的有限抽样特性。