Multi-Cause Effect Estimation with Disentangled Confounder Representation

One fundamental problem in causality learning is to estimate the causal effects of one or multiple treatments (e. g. , medicines in the prescription) on an important outcome (e. g. , cure of a disease). One major challenge of causal effect estimation is the existence of unobserved confounders -- the unobserved variables that affect both the treatments and the outcome. Recent studies have shown that by modeling how instances are assigned with different treatments together, the patterns of unobserved confounders can be captured through their learned latent representations. However, the interpretability of the representations in these works is limited. In this paper, we focus on the multi-cause effect estimation problem from a new perspective by learning disentangled representations of confounders. The disentangled representations not only facilitate the treatment effect estimation but also strengthen the understanding of causality learning process. Experimental results on both synthetic and real-world datasets show the superiority of our proposed framework from different aspects.

Authors

• Jing Ma Xidian University
• Ruocheng Guo
• Aidong Zhang University of Virginia
• Jundong Li University of Virginia

Keywords

• AI Ethics, Trust, Fairness: Trustable Learning
• Machine Learning: Explainable/Interpretable Machine Learning