ACE: Adaptively Similarity-Preserved Representation Learning for Individual Treatment Effect Estimation
ACE: Adaptively Similarity-Preserved Representation Learning for Individual Treatment Effect Estimation
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DOI：
10.1109/icdm.2019.00186
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Liuyi Yao;Sheng Li;Yaliang Li;Mengdi Huai;Jing Gao;Aidong Zhang
中科院分区：
文献类型：
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作者：
Liuyi Yao;Sheng Li;Yaliang Li;Mengdi Huai;Jing Gao;Aidong Zhang
治疗效果估计是对因果效应的估计，它有利于跨领域的决策过程，但在真实的实践中是一个具有挑战性的问题。从个体水平的观测数据估计因果效应面临两个主要挑战，即，治疗选择偏差和缺失的反事实。现有的方法通过学习一个平衡的表示来解决选择偏差问题，并根据学习到的表示来推断缺失的反事实。然而，大多数现有的方法以全局方式学习表示，忽略了局部相似性信息，这对于准确估计因果效应是必不可少的。受上述观察的启发，我们提出了一种新的表示学习方法，该方法自适应地从原始特征空间中提取细粒度的相似性信息，并最小化不同治疗组之间的距离以及表示学习过程中的相似性损失。在三个公开数据集上的实验表明，该方法在所有比较方法中具有最佳的因果效应估计性能，并且对治疗选择偏差具有鲁棒性。
Treatment effect estimation refers to the estimation of causal effects, which benefits decision-making process across various domains, but it is a challenging problem in real practice. The estimation of causal effects from observational data at the individual level faces two major challenges, i.e., treatment selection bias and missing counterfactuals. Existing methods tackle the selection bias problem by learning a balanced representation and infer the missing counterfactuals based on the learned representation. However, most existing methods learn the representation in a global manner and ignore the local similarity information, which is essential for an accurate estimation of causal effects. Motivated by the above observations, we propose a novel representation learning method, which adaptively extracts fine-grained similarity information from the original feature space and minimizes the distance between different treatment groups as well as the similarity loss during the representation learning procedure. Experiments on three public datasets demonstrate that the proposed method achieves the best performance in causal effect estimation among all the compared methods and is robust to the treatment selection bias.