Abstract
Causal discovery from data affected by latent confounders is an important and difficult challenge. Causal functional model-based approaches have not been used to present variables whose relationships are affected by latent confounders, while some constraint-based methods can present them. This paper proposes a causal functional model-based method called repetitive causal discovery (RCD) to discover the causal structure of observed variables affected by latent confounders. RCD repeats inferring the causal directions between a small number of observed variables and determines whether the relationships are affected by latent confounders. RCD finally produces a causal graph where a bidirected arrow indicates the pair of variables that have the same latent confounders and a directed arrow indicates the causal direction of a pair of variables that are not affected by the same latent confounder. The results of experimental validation using simulated data and real-world data confirmed that RCD is effective in identifying latent confounders and causal directions between observed variables.
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Acknowledgements
We thank Dr. Samuel Y. Wang for his useful comments on a previous version of our algorithm proposed in [9]. Takashi Nicholas Maeda has been partially supported by Grant-in-Aid for Scientific Research (C) from Japan Society for the Promotion of Science (JSPS) #20K19872. Shohei Shimizu has been partially supported by ONRG NICOP N62909-17-1-2034 and Grant-in-Aid for Scientific Research (C) from Japan Society for the Promotion of Science (JSPS) #16K00045 and #20K11708.
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Maeda, T.N., Shimizu, S. Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders. Int J Data Sci Anal 13, 77–89 (2022). https://doi.org/10.1007/s41060-021-00282-0
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DOI: https://doi.org/10.1007/s41060-021-00282-0