Optimal transport weights for causal inference
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用于因果推断的最优传输权重
- 研究问题
- 观察性研究中，如何在未知处理分配机制时直接平衡协变量分布，减少选择权重模型及平衡函数带来的不确定性？
- 方法与思路
- 作者提出因果最优传输，通过最小化处理组与对照组或来源与目标总体间的传输距离构造权重，也允许加入矩约束。
- 材料陈述的贡献
- 摘要称方法具有半参数效率，并给出条件均值估计与潜在结局插补的收敛率；在所述模型误设比较中表现较好。
- 阅读关注点
- 核对传输距离、权重约束及目标效应的定义，重点检查半参数效率与收敛率的条件，以及外部对照试验的识别依据。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未列识别假设、具体收敛率及比较设置，不能据此认定普遍优越。
原文摘要
Imbalance in covariate distributions leads to biased estimates of causal effects. Weighting methods attempt to correct this imbalance but rely on specifying models for the treatment assignment mechanism, which is unknown in observational studies. This leaves researchers to choose the proper weighting method and the appropriate covariate functions for these models without knowing the correct combination to achieve distributional balance. In response to these difficulties, we propose a nonparametric generalization of several other weighting schemes found in the literature: Causal Optimal Transport. This new method directly targets distributional balance by minimizing optimal transport distances between treatment and control groups or, more generally, between any source and target population. Our approach is semiparametrically efficient and model-free but can also incorporate moments or any other important functions of covariates that a researcher desires to balance. Moreover, our method can provide nonparametric estimate the conditional mean outcome function and we give rates for the convergence of this estimator. Moreover, we show how this method can provide nonparametric imputations of the missing potential outcomes and give rates of convergence for this estimator. We find that Causal Optimal Transport outperforms competitor methods when both the propensity score and outcome models are misspecified, indicating it is a robust alternative to common weighting methods. Finally, we demonstrate the utility of our method in an external control trial examining the effect of misoprostol versus oxytocin for the treatment of post-partum hemorrhage.