论文标题
有针对性的VAE：因果推断的变异和目标学习
Targeted VAE: Variational and Targeted Learning for Causal Inference
论文作者
论文摘要
在各种任务中，使用观察数据进行因果推断非常有用，包括医疗治疗，广告，市场营销以及政策制定。使用观察数据进行因果推断有两个重大挑战：治疗分配异质性（\ textIt {i.e。}，处理过的组和未经治疗的组之间的差异），以及缺乏反事实数据（\ textit {i.e.e。}，不知道会发生什么，如果一个人不知道会受到治疗，那是什么）。我们通过结合结构化推理和有针对性的学习来应对这两个挑战。在结构方面，我们将联合分布分配为风险，混淆，工具和杂项因素，而在目标学习方面，我们应用了从影响曲线中得出的常规器，以减少残留偏见。进行了一项消融研究，对基准数据集进行评估表明，TVAE具有竞争性和最新性能的状态。
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.
