Chain graph models and their causal interpretations
نویسندگان
چکیده
منابع مشابه
Chain Graph Interpretations and Their Relations
This paper deals with different chain graph interpretations and the relations between them in terms of representable independence models. Specifically, we study the Lauritzen-Wermuth-Frydenberg, Andersson-Madigan-Pearlman and multivariate regression interpretations and present the necessary and sufficient conditions for when a chain graph of one interpretation can be perfectly translated into a...
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Probabilistic graphical models are today one of the most well used architec-tures for modelling and reasoning about knowledge with uncertainty. Themost widely used subclass of these models is Bayesian networks that hasfound a wide range of applications both in industry and research. Bayesiannetworks do however have a major limitation which is that only asymmetricrelationship...
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Abstract. Starting from the observation that Friedman’s partial dependence plot has exactly the same formula as Pearl’s back-door adjustment, we explore the possibility of extracting causal information from black-box models trained by machine learning algorithms. There are three requirements to make causal interpretations: a model with good predictive performance, some domain knowledge in the f...
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ژورنال
عنوان ژورنال: Journal of the Royal Statistical Society: Series B (Statistical Methodology)
سال: 2002
ISSN: 1369-7412,1467-9868
DOI: 10.1111/1467-9868.00340