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• W2980498914 abstract "We consider the task of causal structure learning over measurement dependence inducing latent (MeDIL) causal models. We show that this task can be framed in terms of the graph theoretic problem of finding edge clique covers,resulting in an algorithm for returning minimal MeDIL causal models (minMCMs). This algorithm is non-parametric, requiring no assumptions about linearity or Gaussianity. Furthermore, despite rather weak assumptions aboutthe class of MeDIL causal models, we show that minimality in minMCMs implies some rather specific and interesting properties. By establishing MeDIL causal models as a semantics for edge clique covers, we also provide a starting point for future work further connecting causal structure learning to developments in graph theory and network science." @default.
• W2980498914 created "2019-10-25" @default.
• W2980498914 creator A5021142859 @default.
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• W2980498914 date "2019-10-19" @default.
• W2980498914 modified "2023-09-27" @default.
• W2980498914 title "Measurement Dependence Inducing Latent Causal Models" @default.
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• W2980498914 hasPublicationYear "2019" @default.
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