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Erschienen in:
1996 | OriginalPaper | Buchkapitel
Likelihood-based Causal Inference
verfasst von : Qing Yao, David Tritchler
Erschienen in: Learning from Data
Verlag: Springer New York
Enthalten in: Professional Book Archive
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A method is given which uses subject matter assumptions to discriminate recursive models and thus point toward possible causal explanations. The assumptions alone do not specify any order among the variables — rather just a theoretical absence of direct association. We show how these assumptions, while not specifying any ordering, can when combined with the data through the likelihood function yield information about an underlying recursive order. We derive details of the method for multi-normal random variables.