Learning Decomposed Representations for Treatment Effect Estimation
Learning Decomposed Representations for Treatment Effect Estimation
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DOI：
10.1109/tkde.2022.3150807
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发表时间：
2023-05
影响因子：
8.9
通讯作者：
Anpeng Wu;Junkun Yuan;Kun Kuang;B. Li;Runze Wu;Qiang Zhu;Yueting Zhuang;Fei Wu
中科院分区：
文献类型：
--
作者：
Anpeng Wu;Junkun Yuan;Kun Kuang;B. Li;Runze Wu;Qiang Zhu;Yueting Zhuang;Fei Wu
在观察性研究中，混杂因素的分离和平衡是疗效估计的基本问题。大多数以前的方法集中在解决混杂因素平衡的问题，通过将所有观察到的治疗前变量作为混杂因素，忽略混杂因素分离。一般而言，并非所有观察到的治疗前变量都是指治疗和结局的常见原因的混杂因素，一些变量仅有助于治疗（即，工具变量）和一些仅对结果有贡献（即，调整变量）。平衡这些非混杂因素，包括工具变量和调整变量，将产生治疗效果估计的额外偏倚。通过对观察到的治疗前变量、治疗变量和结果变量之间的不同因果关系进行建模，我们提出了一个协同学习框架，以i）通过学习混杂因素和非混杂因素的分解表示来分离混杂因素，ii）通过样本重新加权技术来平衡混杂因素，同时iii）通过反事实推理来估计观察性研究中的治疗效果。在合成数据集和真实数据集上的实验结果表明，该方法可以精确地分解混杂因素，并实现比基线更精确的治疗效果估计。
In observational studies, confounder separation and balancing are the fundamental problems of treatment effect estimation. Most of the previous methods focused on addressing the problem of confounder balancing by treating all observed pre-treatment variables as confounders, ignoring confounder separation. In general, not all the observed pre-treatment variables are confounders that refer to the common causes of the treatment and the outcome, some variables only contribute to the treatment (i.e., instrumental variables) and some only contribute to the outcome (i.e., adjustment variables). Balancing those non-confounders, including instrumental variables and adjustment variables, would generate additional bias for treatment effect estimation. By modeling the different causal relations among observed pre-treatment variables, treatment variables and outcome variables, we propose a synergistic learning framework to i) separate confounders by learning decomposed representations of both confounders and non-confounders, ii) balance confounder with sample re-weighting technique, and simultaneously iii) estimate the treatment effect in observational studies via counterfactual inference. Empirical results on synthetic and real-world datasets demonstrate that the proposed method can precisely decompose confounders and achieve a more precise estimation of treatment effect than baselines.