Chapter

When are graphical causal models not good models?

Jan Lemeire, Kris Steenhaut and Abdellah Touhafi

in Causality in the Sciences

Published in print March 2011 | ISBN: 9780199574131
Published online September 2011 | e-ISBN: 9780191728921 | DOI: http://dx.doi.org/10.1093/acprof:oso/9780199574131.003.0027
When are graphical causal models not good models?

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The principle of Kolmogorov minimal sufficient statistic (KMSS) states that the meaningful information of data is given by the regularities in the data. The KMSS is the minimal model that describes the regularities. The meaningful information given by a Bayesian network is the directed acyclic graph (DAG) which describes a decomposition of the joint probability distribution into conditional probability distributions (CPDs). If the description given by the Bayesian network is incompressible, the DAG is the KMSS and is faithful. The chapter proves that if a faithful Bayesian network exists, it is the minimal Bayesian network. Moreover, if a Bayesian network gives the KMSS, modularity of the CPDs is the most plausible hypothesis, from which the causal interpretation follows. On the other hand, if the minimal Bayesian network is compressible and is thus not the KMSS, the above implications cannot be guaranteed. When the non‐minimality of the description is due to the compressibility of an individual CPD, the true causal model is an element of the set of minimal Bayesian networks and modularity is still plausible. Faithfulness cannot be guaranteed though. When the concatenation of the descriptions of the CPDs is compressible, the true causal model is not necessarily an element of the set of minimal Bayesian networks. Also modularity may become implausible. This suggests that either there is a kind of meta‐mechanism governing some of the mechanisms or a wrong model class is considered.

Keywords: causal models; Bayesian networks; Kolmogorov complexity; Kolmogorov minimal sufficient statistic

Chapter.  9102 words.  Illustrated.

Subjects: Logic

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