Differentiated matching for individual and average treatment effect estimation
Differentiated matching for individual and average treatment effect estimation
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DOI：
10.1007/s10618-022-00886-5
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发表时间：
2022-11
影响因子：
4.8
通讯作者：
Zhao Ziyu;Kun Kuang;Bo Li;Peng Cui;Runze Wu;Jun Xiao;Fei Wu
中科院分区：
文献类型：
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作者：
Zhao Ziyu;Kun Kuang;Bo Li;Peng Cui;Runze Wu;Jun Xiao;Fei Wu
因果推断的一个基本问题是用变量混淆的观测数据估计处理效果。控制混杂偏差的传统方法是将具有不同处理但变量相似的单元进行匹配。然而，传统的匹配方法无法在众多潜在混杂因素池中进行选择和区分，从而可能导致性能不佳。本文对混杂差分法进行了理论分析，提出了一种新的用于个体和平均治疗效果估计的差异化匹配(DM)算法，该算法通过学习变量差分和单元匹配的混杂权重来估计个体和平均疗效。为了解决混杂权重学习中的分布漂移问题，本文进一步提出了一种基于倾向性得分的加权回归DM(PSDM)算法。在合成数据集和真实数据集上的大量实验表明，所提出的算法在治疗效果估计上取得了比其他匹配方法更好的性能。
One fundamental problem of causal inference is estimating treatment eect with observational data where variables are confounded. The traditional way of controlling the confounding bias is to match units with different treatments but similar variables. However, traditional matching methods fail on selection and differentiation among the pool of numerous potential confounders, leading to possible under-performance. In this paper, we give a theoretical analysis of confounder differentiation and propose a novel Differentiated Matching (DM) algorithm for both individual and average treatment effect estimation by learning confounder weights for variable differentiation and unit matching. To address the distribution shift in confounder weights learning, we further propose a Propensity Score based DM (PSDM) algorithm by weighted regression with the inverse of the propensity score. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms achieve better performance than other matching methods on treatment effect estimation.