On the Intersection Property of Conditional Independence and its Application to Causal Discovery

Peters Jonas ()
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Peters Jonas: ETH Zürich, Switzerland

Journal of Causal Inference, 2015, vol. 3, issue 1, 97-108

Abstract: This work investigates the intersection property of conditional independence. It states that for random variables A,B,C$$A,B,C$$ and X we have that X⊥⊥A|B,C$$X \bot \bot A{\kern 1pt} {\kern 1pt} |{\kern 1pt} {\kern 1pt} B,C$$ and X⊥⊥B|A,C$$X\, \bot \bot\, B{\kern 1pt} {\kern 1pt} |{\kern 1pt} {\kern 1pt} A,C$$ implies X⊥⊥(A,B)|C$$X\, \bot \bot\, (A,B){\kern 1pt} {\kern 1pt} |{\kern 1pt} {\kern 1pt} C$$. Here, “⊥⊥$$ \bot \bot $$” stands for statistical independence. Under the assumption that the joint distribution has a density that is continuous in A,B$$A,B$$ and C, we provide necessary and sufficient conditions under which the intersection property holds. The result has direct applications to causal inference: it leads to strictly weaker conditions under which the graphical structure becomes identifiable from the joint distribution of an additive noise model.

Keywords: probability theory; causal discovery; graphical models (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:3:y:2015:i:1:p:97-108:n:1005

DOI: 10.1515/jci-2014-0015

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