Much research has been devoted to the problem of estimating treatment effects from observational data; however, most methods assume that the observed variables only contain confounders, i.e., variables that affect both the treatment and the outcome. Unfortunately, this assumption is frequently violated in real-world applications, since some variables only affect the treatment but not the outcome, and vice versa. Moreover, in many cases only the proxy variables of the underlying confounding factors can be observed. In this work, we first show the importance of differentiating confounding factors from instrumental and risk factors for both average and conditional average treatment effect estimation, and then we propose a variational inference approach to simultaneously infer latent factors from the observed variables, disentangle the factors into three disjoint sets corresponding to the instrumental, confounding, and risk factors, and use the disentangled factors for treatment effect estimation. Experimental results demonstrate the effectiveness of the proposed method on a wide range of synthetic, benchmark, and real-world datasets.

翻译：对从观察数据中估计治疗效果的问题进行了大量研究;然而,大多数方法都假定观测到的变量只包含混杂因素,即影响治疗和结果的变量;不幸的是,这种假设在现实应用中经常被违反,因为有些变量只影响治疗,而不是结果,反之亦然。此外,在许多情况下,只能观察到基本混杂因素的替代变量。在这项工作中,我们首先表明必须区分平均和有条件平均治疗效果估计中的因素和风险因素,然后我们提出一种变式推论方法,以同时从观察到的变量中推断潜在因素,将各种因素分解成三种与工具、混杂因素和风险因素相对的脱节,并利用分解因素来估计治疗效果。实验结果表明拟议方法在广泛的合成、基准和实际世界数据集中的有效性。