Over the past few decades, a number of methods have been proposed for causal effect estimation, yet few have been demonstrated to be effective in handling data with complex structures, such as images. To fill this gap, we propose a Causal Multi-task Deep Ensemble (CMDE) framework to learn both shared and group-specific information from the study population and prove its equivalence to a multi-task Gaussian process (GP) with coregionalization kernel a priori. Compared to multi-task GP, CMDE efficiently handles high-dimensional and multi-modal covariates and provides pointwise uncertainty estimates of causal effects. We evaluate our method across various types of datasets and tasks and find that CMDE outperforms state-of-the-art methods on a majority of these tasks.

翻译：摘要：过去几十年中，学界提出了多种因果效应估计方法，但鲜有方法被证明能有效处理图像等具有复杂结构的数据。为填补这一空白，我们提出因果多任务深度集成（CMDE）框架，用于从研究人群中学习共享信息与组特有信息，并证明了该框架先验等价于带有协同区域化核的多任务高斯过程。相较于多任务GP，CMDE能高效处理高维及多模态协变量，并提供因果效应的逐点不确定性估计。我们在多种类型的数据集和任务上评估了该方法，结果表明CMDE在大部分任务中均优于现有最先进方法。