论文标题
通过基于优化的方法估算因果效应：综述和经验比较
Estimating causal effects with optimization-based methods: A review and empirical comparison
论文作者
论文摘要
在没有随机对照和自然实验的情况下，有必要平衡处理组和对照组的（可观察）协变量的分布，以获得对利益因果关系的无偏估计。否则，可以估算不同的效果大小，并且可能会提供错误的建议。为了达到这种平衡，存在多种方法。特别是，最近在因果推理文献中提出了基于优化模型的几种方法。尽管这些基于优化的方法在经验上表现出比数量有限的其他因果推断方法的改善，其相对能力平衡了协变量的分布并估算因果效应，但它们尚未得到彼此的及其和其他值得注意的因果推理方法的彻底比较。此外，我们认为，运营研究人员可以为使用因果推理工具的应用研究人员的好处而贡献几个未解决的机会。在本综述论文中，我们介绍了因果推理文献的概述，并更详细地描述了基于优化的因果推理方法，提供了对基于优化的方法的比较分析，并讨论了新方法的机会。
In the absence of randomized controlled and natural experiments, it is necessary to balance the distributions of (observable) covariates of the treated and control groups in order to obtain an unbiased estimate of a causal effect of interest; otherwise, a different effect size may be estimated, and incorrect recommendations may be given. To achieve this balance, there exist a wide variety of methods. In particular, several methods based on optimization models have been recently proposed in the causal inference literature. While these optimization-based methods empirically showed an improvement over a limited number of other causal inference methods in their relative ability to balance the distributions of covariates and to estimate causal effects, they have not been thoroughly compared to each other and to other noteworthy causal inference methods. In addition, we believe that there exist several unaddressed opportunities that operational researchers could contribute with their advanced knowledge of optimization, for the benefits of the applied researchers that use causal inference tools. In this review paper, we present an overview of the causal inference literature and describe in more detail the optimization-based causal inference methods, provide a comparative analysis of the prevailing optimization-based methods, and discuss opportunities for new methods.
