Learning causal effects from observational data greatly benefits a variety of domains such as health care, education, and sociology. For instance, one could estimate the impact of a new drug on specific individuals to assist clinical planning and improve the survival rate. In this paper, we focus on studying the problem of estimating the Conditional Average Treatment Effect (CATE) from observational data. The challenges for this problem are two-fold: on the one hand, we have to derive a causal estimator to estimate the causal quantity from observational data, in the presence of confounding bias; on the other hand, we have to deal with the identification of the CATE when the distributions of covariates over the treatment group units and the control units are imbalanced. To overcome these challenges, we propose a neural network framework called Adversarial Balancing-based representation learning for Causal Effect Inference (ABCEI), based on recent advances in representation learning. To ensure the identification of the CATE, ABCEI uses adversarial learning to balance the distributions of covariates in the treatment and the control group in the latent representation space, without any assumptions on the form of the treatment selection/assignment function. In addition, during the representation learning and balancing process, highly predictive information from the original covariate space might be lost. ABCEI can tackle this information loss problem by preserving useful information for predicting causal effects under the regularization of a mutual information estimator. The experimental results show that ABCEI is robust against treatment selection bias, and matches/outperforms the state-of-the-art approaches. Our experiments show promising results on several datasets, encompassing several health care (and other) domains.

从观测数据中学习因果关系会极大地有益于医疗，教育和社会学等各个领域。例如，可以估计一种新药对特定个体的影响，以协助临床规划并提高生存率。在本文中，我们专注于研究根据观察数据估算条件平均治疗效果（CATE）的问题。这个问题的挑战有两个方面：一方面，在存在混淆性偏见的情况下，我们必须推导因果估计量，以便根据观测数据估计因果量；另一方面，当治疗组单位和控制单位之间的协变量分布不平衡时，我们必须处理CATE的识别。为了克服这些挑战，我们基于表示学习的最新进展，提出了一种因果效应推理（ABCEI）的神经网络框架，称为基于对抗平衡的表示学习。为了确保识别CATE，ABCEI使用对抗学习来平衡潜在代表空间中治疗组和对照组中协变量的分布，而无需假设治疗选择/分配功能的形式。此外，在表示学习和平衡过程中，可能会丢失来自原始协变量空间的高度预测性信息。ABCEI可以通过在互信估计器的正则化条件下保留有用的信息来预测因果关系来解决此信息丢失问题。实验结果表明，ABCEI可以抵抗治疗选择偏倚，并且匹配/优于最新技术。我们的实验在包含多个医疗保健（和其他）领域的几个数据集上显示了令人鼓舞的结果。