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• W4321330689 abstract "Bayesian networks have been widely used to generate causal hypotheses from multivariate data. Despite their popularity, the vast majority of existing causal discovery approaches make the strong assumption of a (partially) homogeneous sampling scheme. However, such assumption can be seriously violated, causing significant biases when the underlying population is inherently heterogeneous. To this end, we propose a novel causal Bayesian network model, termed BN-LTE, that embeds heterogeneous samples onto a low-dimensional manifold and builds Bayesian networks conditional on the embedding. This new framework allows for more precise network inference by improving the estimation resolution from the population level to the observation level. Moreover, while causal Bayesian networks are in general not identifiable with purely observational, cross-sectional data due to Markov equivalence, with the blessing of causal effect heterogeneity, we prove that the proposed BN-LTE is uniquely identifiable under relatively mild assumptions. Through extensive experiments, we demonstrate the superior performance of BN-LTE in causal structure learning as well as inferring observation-specific gene regulatory networks from observational data." @default.
• W4321330689 created "2023-02-19" @default.
• W4321330689 creator A5018271417 @default.
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• W4321330689 date "2023-03-15" @default.
• W4321330689 modified "2023-10-14" @default.
• W4321330689 title "Individualized causal discovery with latent trajectory embedded Bayesian networks" @default.
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• W4321330689 doi "https://doi.org/10.1111/biom.13843" @default.
• W4321330689 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36807295" @default.
• W4321330689 hasPublicationYear "2023" @default.
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