Uncertainty in Artificial Intelligence
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Causal Inference by Surrogate Experiments: z-Identifiability
Elias Bareinboim, Judea Pearl
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 = empty 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 outcomes). 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.
Pages: 113-120
PS Link:
PDF Link: /papers/12/p113-bareinboim.pdf
AUTHOR = "Elias Bareinboim and Judea Pearl",
TITLE = "Causal Inference by Surrogate Experiments: z-Identifiability",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "113--120"

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