Estimating Causal Effects using a Multi-task Deep Ensemble
This addresses the challenge of handling high-dimensional and multi-modal data in causal inference, which is important for researchers and practitioners in fields like healthcare or social sciences, though it appears incremental as it builds on existing multi-task methods.
The paper tackled the problem of causal effect estimation with complex data like images by proposing the Causal Multi-task Deep Ensemble (CMDE) framework, which outperformed state-of-the-art methods on most tasks in evaluations.
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.