Representation Learning for Treatment Effect Estimation from Observational Data
Representation Learning for Treatment Effect Estimation from Observational Data
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发表时间：
2018
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通讯作者：
Liuyi Yao;Sheng Li;Yaliang Li;Mengdi Huai;Jing Gao;Aidong Zhang
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作者：
Liuyi Yao;Sheng Li;Yaliang Li;Mengdi Huai;Jing Gao;Aidong Zhang
个体治疗效应（ITE）的估计是因果推理中的一个挑战性问题，因为存在着反事实的缺失和选择偏倚。现有的ITE估计方法主要集中在平衡对照组和治疗组的分布，而忽略了有用的局部相似性信息。本文提出了一种基于深度表征学习的局部相似性保持个体治疗效果（SITE）估计方法。SITE通过关注每个小批次中的几个硬样本，同时保持局部相似性和平衡数据分布。合成和三个真实世界的数据集上的实验结果表明，所提出的SITE方法的优势，与国家的最先进的ITE估计方法相比。
Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due to the missing counterfactuals and the selection bias. Existing ITE estimation methods mainly focus on balancing the distributions of control and treated groups, but ignore the local similarity information that is helpful. In this paper, we propose a local similarity preserved individual treatment effect (SITE) estimation method based on deep representation learning. SITE preserves local similarity and balances data distributions simultaneously, by focusing on several hard samples in each mini-batch. Experimental results on synthetic and three real-world datasets demonstrate the advantages of the proposed SITE method, compared with the state-of-the-art ITE estimation methods.