Estimating causal effects with optimization-based methods: A review and empirical comparison
基于优化的因果效应估计方法：综述与实证比较AI 解读 ↓
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基于优化的因果效应估计方法：综述与实证比较
- 研究问题
- 在缺少随机实验或自然实验时，各类优化方法如何平衡处理组与对照组的可观测协变量，其因果效应估计表现有何差异？
- 方法与思路
- 作者综述因果推断文献，重点介绍基于优化的协变量平衡方法，比较主要方法，并讨论运筹优化知识可能带来的新研究机会。
- 材料陈述的贡献
- 摘要明确提出对现有优化型因果推断方法进行系统梳理和比较，回应此前相互比较不足的问题，并讨论进一步开发方法的方向。
- 阅读关注点
- 应核对各方法对应的目标效应、平衡标准、比较设计和评价指标，尤其区分协变量平衡改善与因果效应识别所需的条件。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审或接收未核实；摘要未说明具体比较结果，可观测协变量平衡本身不能排除未观测混杂。
原文摘要
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.