Randomized controlled trials (RCTs) are the accepted standard for treatment effect estimation but they can be infeasible due to ethical reasons and prohibitive costs. Single-arm trials, where all patients belong to the treatment group, can be a viable alternative but require access to an external control group. We propose an identifiable deep latent-variable model for this scenario that can also account for missing covariate observations by modeling their structured missingness patterns. Our method uses amortized variational inference to learn both group-specific and identifiable shared latent representations, which can subsequently be used for {\em (i)} patient matching if treatment outcomes are not available for the treatment group, or for {\em (ii)} direct treatment effect estimation assuming outcomes are available for both groups. We evaluate the model on a public benchmark as well as on a data set consisting of a published RCT study and real-world electronic health records. Compared to previous methods, our results show improved performance both for direct treatment effect estimation as well as for effect estimation via patient matching.

翻译：随机对照试验（RCTs）是治疗效果估计的公认标准，但由于伦理原因和过高成本可能难以实施。单臂试验（所有患者均属于治疗组）可作为可行替代方案，但需要获取外部对照组。针对这一场景，我们提出了一种可识别的深度潜变量模型，该模型还能通过建模协变量缺失的结构化模式来处理缺失观测值。我们的方法利用摊销变分推断来学习分组特异性与可识别的共享潜变量表示，这些表示可进一步用于：（i）治疗组缺乏治疗结局时的患者匹配，或（ii）假设两组均有结局数据时的直接治疗效果估计。我们在公开基准数据集和包含已发表RCT研究及真实世界电子健康记录的数据集上评估了该模型。与先前方法相比，我们的结果在直接治疗效果估计和基于患者匹配的效果估计中均展现出更优性能。