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When are Graphical Models not Good Models

In Phyllis McKay Illari Federica Russo (ed.), Causality in the Sciences. Oxford University Press (2011)

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  1. Replacing Causal Faithfulness with Algorithmic Independence of Conditionals.Jan Lemeire & Dominik Janzing - 2013 - Minds and Machines 23 (2):227-249.
    Independence of Conditionals (IC) has recently been proposed as a basic rule for causal structure learning. If a Bayesian network represents the causal structure, its Conditional Probability Distributions (CPDs) should be algorithmically independent. In this paper we compare IC with causal faithfulness (FF), stating that only those conditional independences that are implied by the causal Markov condition hold true. The latter is a basic postulate in common approaches to causal structure learning. The common spirit of FF and IC is to (...)
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