Abstract
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.
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© 1996 Springer-Verlag New York, Inc.
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Yao, Q., Tritchler, D. (1996). Likelihood-based Causal Inference. In: Fisher, D., Lenz, HJ. (eds) Learning from Data. Lecture Notes in Statistics, vol 112. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2404-4_4
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DOI: https://doi.org/10.1007/978-1-4612-2404-4_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-94736-5
Online ISBN: 978-1-4612-2404-4
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