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• W3092002364 abstract "We consider the problem of learning structured causal models from observational data. In this work, we use causal Bayesian networks to represent causal relationships among model variables. To this effect, we explore the use of two types of independencies -- context-specific independence (CSI) and mutual independence (MI). We use CSI to identify the candidate set of causal relationships and then use MI to quantify their strengths and construct a causal model. We validate the learned models on benchmark networks and demonstrate the effectiveness when compared to some of the state-of-the-art Causal Bayesian Network Learning algorithms from observational Data." @default.
• W3092002364 created "2020-10-15" @default.
• W3092002364 creator A5064323671 @default.
• W3092002364 creator A5078428949 @default.
• W3092002364 date "2020-10-07" @default.
• W3092002364 modified "2023-09-27" @default.
• W3092002364 title "Causal Learning From Predictive Modeling for Observational Data" @default.
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• W3092002364 doi "https://doi.org/10.3389/fdata.2020.535976" @default.
• W3092002364 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7931928" @default.
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• W3092002364 hasPublicationYear "2020" @default.
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