Undertaking causal inference with observational data is incredibly useful across a wide range of tasks including the development of medical treatments, advertisements and marketing, and policy making. There are two significant challenges associated with undertaking causal inference using observational data: treatment assignment heterogeneity (\textit{i.e.}, differences between the treated and untreated groups), and an absence of counterfactual data (\textit{i.e.}, not knowing what would have happened if an individual who did get treatment, were instead to have not been treated). We address these two challenges by combining structured inference and targeted learning. In terms of structure, we factorize the joint distribution into risk, confounding, instrumental, and miscellaneous factors, and in terms of targeted learning, we apply a regularizer derived from the influence curve in order to reduce residual bias. An ablation study is undertaken, and an evaluation on benchmark datasets demonstrates that TVAE has competitive and state of the art performance.
翻译:对观测数据进行因果推断在包括发展医疗、广告和营销以及决策在内的广泛任务中是极其有用的。在利用观察数据进行因果推断方面,存在着两个重大挑战:治疗分配差异(\textit{i.e.),治疗群体和未治疗群体之间的差异,以及缺乏反事实数据(\textit{i.e.}),不知道如果一个人得到治疗,而没有得到治疗,就会发生什么。我们通过将结构化推论和有针对性的学习结合起来来应对这两个挑战。在结构上,我们将联合分布因素分为风险、汇合、工具因素和杂杂变因素,在有目标的学习方面,我们采用从影响曲线中得来的定律,以减少剩余偏差。我们进行了反差研究,对基准数据集的评估表明TVAE具有竞争力和艺术表现状况。