Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e.g. gene expressions, impulse responses, human faces) and covariates are relatively limited. In this case, to construct one's outcome under a counterfactual treatment, it is crucial to leverage individual information contained in its observed factual outcome on top of the covariates. We propose a deep variational Bayesian framework that rigorously integrates two main sources of information for outcome construction under a counterfactual treatment: one source is the individual features embedded in the high-dimensional factual outcome; the other source is the response distribution of similar subjects (subjects with the same covariates) that factually received this treatment of interest.

翻译：在反事实治疗下估计个体的潜在结果是一项具有挑战性的任务，当结果具有高维性（例如，基因表达、脉冲响应、人脸）且协变量相对有限时，传统的因果推断和监督学习方法难以应对。在这种情况下，为了构建个体在反事实治疗下的结果，除了协变量之外，必须利用其观察到的事实结果中包含的个体信息。我们提出了一个深度变分贝叶斯框架，该框架严格整合了在反事实治疗下构建结果的两个主要信息来源：一个来源是嵌入在高维事实结果中的个体特征；另一个来源是实际接受该治疗且具有相同协变量的相似个体的反应分布。