论文标题
治疗效应估计与分解潜在因素
Treatment effect estimation with disentangled latent factors
论文作者
论文摘要
许多研究都致力于估计观察数据的治疗效果的问题。但是，大多数方法都认为观察到的变量仅包含混杂因素，即影响治疗和结果的变量。不幸的是，在现实世界应用中经常违反该假设，因为某些变量仅影响治疗，但不影响结果，反之亦然。此外，在许多情况下，只能观察到基本混杂因素的代理变量。在这项工作中，我们首先显示了将混杂因素与工具和风险因素区分开来对平均和有条件的平均治疗效应估计的重要性，然后我们提出了一种变异的推理方法来同时推断观察到的变量，将因素分解为与工具，混淆，混淆和使用效果相关的因素，并使用这些因素，并使用这些因素，并使用这些因素不同意。实验结果证明了该方法对广泛的合成，基准和现实世界数据集的有效性。
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.
