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 (i.e., differences between the treated and untreated groups), and an absence of counterfactual data (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:对观察数据进行因果关系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关系推断方面,存在着两个重大挑战:治疗分配差异(即受治疗群体和未受治疗群体之间的差异),以及缺乏反事实数据(即不知道如果接受治疗的个人得不到治疗会发生什么,而没有得到治疗);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项挑战。在结构方面,我们将联合分布因素分为风险、混杂、工具性和杂项因素,在有针对性学习方面,我们采用从影响曲线中得出的定期调节器来减少残余偏差。进行减缩研究,对基准数据集的评估表明TVAE有竞争力和艺术表现状况。