
Causal Transportability of Experiments on Controllable Subsets of Variables: zTransportability
We introduce ztransportability, the problem of estimating the causal ef...
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Causal inference with Bayes rule
The concept of causality has a controversial history. The question of wh...
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Surrogate Outcomes and Transportability
Identification of causal effects is one of the most fundamental tasks of...
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An Information Theoretic Measure of Judea Pearl's Identifiability and Causal Influence
In this paper, we define a new information theoretic measure that we cal...
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The DoCalculus Revisited
The docalculus was developed in 1995 to facilitate the identification o...
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On sequentiality and wellbracketing in the πcalculus
The π calculus is used as a model for programminglanguages. Its context...
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Replacing the docalculus with Bayes rule
The concept of causality has a controversial history. The question of wh...
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Causal Inference by Surrogate Experiments: zIdentifiability
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 zidentifiability, reduces to ordinary identifiability when Z = empty and, like the latter, can be given syntactic characterization using the docalculus [Pearl, 1995; 2000]. We provide a graphical necessary and sufficient condition for zidentifiability 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 docalculus relative to zidentifiability, a result that does not follow from completeness relative to ordinary identifiability.
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