Estimating Causal Effects using a Multi-task Deep Ensemble
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.
Cite
@article{arxiv.2301.11351,
title = {Estimating Causal Effects using a Multi-task Deep Ensemble},
author = {Ziyang Jiang and Zhuoran Hou and Yiling Liu and Yiman Ren and Keyu Li and David Carlson},
journal= {arXiv preprint arXiv:2301.11351},
year = {2023}
}
Comments
18 pages, 7 figures, 3 tables, published at the 40th International Conference on Machine Learning (ICML 2023)