标题： 结构保持表示学习神经网络用于因果推断 显示英文标题

标题： Structure Maintained Representation Learning Neural Network for Causal Inference

摘要： 最近在因果推断方面的进展大大改变了兴趣从估计平均处理效应转向个体处理效应。 在本文中，我们通过引入一个结构保持器来提高表示学习和对抗网络在估计个体处理效应中的预测准确性，该结构保持器在高维空间中保持基线协变量与其相应表示之间的相关性。 我们在表示层的末端训练一个判别器，以权衡表示平衡和信息损失。 我们证明了所提出的判别器最小化了处理估计误差的上界。 我们可以通过考虑学习到的表示空间与原始协变量特征空间之间的相关性来解决分布平衡和信息损失之间的权衡。 我们进行了广泛的实验，使用模拟和现实世界的观察数据，以表明我们提出的结构保持表示学习（SMRL）算法优于最先进的方法。 我们还在MIMIC-III数据库的真实电子健康记录数据上展示了这些算法。 显示英文摘要

摘要： Recent developments in causal inference have greatly shifted the interest from estimating the average treatment effect to the individual treatment effect. In this article, we improve the predictive accuracy of representation learning and adversarial networks in estimating individual treatment effects by introducing a structure keeper which maintains the correlation between the baseline covariates and their corresponding representations in the high dimensional space. We train a discriminator at the end of representation layers to trade off representation balance and information loss. We show that the proposed discriminator minimizes an upper bound of the treatment estimation error. We can address the tradeoff between distribution balance and information loss by considering the correlations between the learned representation space and the original covariate feature space. We conduct extensive experiments with simulated and real-world observational data to show that our proposed Structure Maintained Representation Learning (SMRL) algorithm outperforms state-of-the-art methods. We also demonstrate the algorithms on real electronic health record data from the MIMIC-III database.