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A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations
[article]
2022
arXiv
pre-print
We consider the problem of causal structure learning in the setting of heterogeneous populations, i.e., populations in which a single causal structure does not adequately represent all population members, as is common in biological and social sciences. To this end, we introduce a distance covariance-based kernel designed specifically to measure the similarity between the underlying nonlinear causal structures of different samples. Indeed, we prove that the corresponding feature map is a
arXiv:2106.03480v2
fatcat:qbmqflgw6zailluwr3nkbh4g3a