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：过去几十年来，因果效应估计方法层出不穷，但鲜有方法被证明能有效处理图像等复杂结构数据。为填补这一空白，我们提出"因果多任务深度集成"（CMDE）框架，从研究人群中学习共享信息与分组特异性信息，并证明其先验等价于具有协同区域化核的多任务高斯过程（GP）。相比多任务GP，CMDE能高效处理高维及多模态协变量，同时提供因果效应的逐点不确定性估计。我们在多种类型数据集和任务上评估该方法，发现CMDE在多数任务上优于现有最优方法。