Proceedings

Causal Discovery with Multi-Domain LiNGAM for Latent Factors

Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 2097-2103.

https://doi.org/10.24963/ijcai.2021/289

Keywords

  • Machine Learning: Learning Graphical Models
  • Knowledge Representation and Reasoning: Action, Change and Causality

Abstract

Discovering causal structures among latent factors from observed data is a particularly challenging problem. Despite some efforts for this problem, existing methods focus on the single-domain data only. In this paper, we propose Multi-Domain Linear Non-Gaussian Acyclic Models for LAtent Factors (MD-LiNA), where the causal structure among latent factors of interest is shared for all domains, and we provide its identification results. The model enriches the causal representation for multi-domain data. We propose an integrated two-phase algorithm to estimate the model. In particular, we first locate the latent factors and estimate the factor loading matrix. Then to uncover the causal structure among shared latent factors of interest, we derive a score function based on the characterization of independence relations between external influences and the dependence relations between multi-domain latent factors and latent factors of interest. We show that the proposed method provides locally consistent estimators. Experimental results on both synthetic and real-world data demonstrate the efficacy and robustness of our approach.

Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, and Zhifeng Hao. 2021. Causal Discovery with Multi-Domain LiNGAM for Latent Factors. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 2097–2103. https://doi.org/10.24963/ijcai.2021/289
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