Bayesian networks can capture causal relations, but learning such a network from data is NP-hard. Recent work has made it possible to approximate this problem as a continuous optimization task that can be solved efficiently with well-established numerical techniques.
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Luo, Y., Peng, J. & Ma, J. When causal inference meets deep learning. Nat Mach Intell 2, 426–427 (2020). https://doi.org/10.1038/s42256-020-0218-x
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DOI: https://doi.org/10.1038/s42256-020-0218-x
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