Estimating Causal Effects using a Multi-task Deep Ensemble

• URL: http://arxiv.org/abs/2301.11351v3
• Date: Sat, 27 May 2023 11:31:52 GMT
• Title: Estimating Causal Effects using a Multi-task Deep Ensemble
• Authors: Ziyang Jiang, Zhuoran Hou, Yiling Liu, Yiman Ren, Keyu Li, David Carlson
• Abstract summary: Causal Multi-task Deep Ensemble (CMDE) is a novel framework that learns both shared and group-specific information from the study population. 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.
• Score: 4.268861137988059
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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