TY - GEN
T1 - Causal Discovery with Multi-Domain LiNGAM for Latent Factors
AU - Zeng, Yan
AU - Shimizu, Shohei
AU - Cai, Ruichu
AU - Xie, Feng
AU - Yamamoto, Michio
AU - Hao, Zhifeng
N1 - Funding Information:
This work was supported by the Grant ONR N00014-20-1-2501, the Natural Science Foundation of China (61876043, 61976052), Science and Technology Planning Project of Guangzhou (201902010058), and the Grant of China Scholarship Council. FX would like to acknowledge the support by China Postdoctoral Science Foundation (020M680225) and a research project of Huawei.
Publisher Copyright:
© 2021 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:85125492343
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 2097
EP - 2103
BT - Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021
A2 - Zhou, Zhi-Hua
PB - International Joint Conferences on Artificial Intelligence
T2 - 30th International Joint Conference on Artificial Intelligence, IJCAI 2021
Y2 - 19 August 2021 through 27 August 2021
ER -