  A number of methods have been proposed for causal effect estimation, yet few
have demonstrated efficacy in handling data with complex structures, such as
images. To fill this gap, we propose Causal Multi-task Deep Ensemble (CMDE), a
novel framework that learns both shared and group-specific information from the
study population. We provide proofs demonstrating equivalency of CDME to a
multi-task Gaussian process (GP) with a 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.
