Proceedings

Multi-Cause Effect Estimation with Disentangled Confounder Representation

Jing Ma, Ruocheng Guo, Aidong Zhang, Jundong Li

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 2790-2796.

https://doi.org/10.24963/ijcai.2021/384

Keywords

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

Abstract

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.

Jing Ma, Ruocheng Guo, Aidong Zhang, and Jundong Li. 2021. Multi-Cause Effect Estimation with Disentangled Confounder Representation. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 2790–2796. https://doi.org/10.24963/ijcai.2021/384
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