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Accession Number ADA564088
Title Causal Inference by Surrogate Experiments: z-Identifiability.
Publication Date Jun 2012
Media Count 9p
Personal Author E. Bareinboim J. Pearl
Abstract We address the problem of estimating the effect of intervening on a set of variables X from experiments on a different set, Z, that is more accessible to manipulation. This problem, which we call z-identifiability reduces to ordinary identifiability when Z = phi and like the latter, can be given syntactic characterization using the do-calculus Pearl, 1995; 2000). We provide a graphical necessary and sufficient condition for z- identifiability for arbitrary sets X,Z, and Y (the out- comes). We further develop a complete algorithm for computing the causal effect of X on Y using information provided by experiments on Z. Finally, we use our results to prove completeness of do- calculus relative to z-identifiability, a result that does not follow from completeness relative to ordinary identifiability.
Keywords Algorithms

Source Agency Non Paid ADAS
NTIS Subject Category 72B - Algebra, Analysis, Geometry, & Mathematical Logic
Corporate Author California Univ., Los Angeles. Cognitive Systems Lab.
Document Type Technical report
Title Note Conference paper.
NTIS Issue Number 1302
Contract Number N00014-09-1-0665 N00014-10-1-0933

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