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- W4301020892 abstract "Probabilistic graphical models are a fundamental tool in statistics, machine learning, signal processing, and control. When such a model is defined on a directed acyclic graph (DAG), one can assign a partial ordering to the events occurring in the corresponding stochastic system. Based on the work of Judea Pearl and others, these DAG-based causal factorizations of joint probability measures have been used for characterization and inference of functional dependencies (causal links). This mostly expository paper focuses on several connections between Pearl's formalism (and in particular his notion of intervention) and information-theoretic notions of causality and feedback (such as causal conditioning, directed stochastic kernels, and directed information). As an application, we show how conditional directed information can be used to develop an information-theoretic version of Pearl's back-door criterion for identifiability of causal effects from passive observations. This suggests that the back-door criterion can be thought of as a causal analog of statistical sufficiency." @default.
- W4301020892 created "2022-10-04" @default.
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- W4301020892 date "2011-10-04" @default.
- W4301020892 modified "2023-09-30" @default.
- W4301020892 title "Directed information and Pearl's causal calculus" @default.
- W4301020892 doi "https://doi.org/10.48550/arxiv.1110.0718" @default.
- W4301020892 hasPublicationYear "2011" @default.
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