2014
NIPS
NeurIPS 2014
Transportability from Multiple Environments with Limited Experiments: Completeness Results
Abstract
This paper addresses the problem of $mz$-transportability, that is, transferring causal knowledge collected in several heterogeneous domains to a target domain in which only passive observations and limited experimental data can be collected. The paper first establishes a necessary and sufficient condition for deciding the feasibility of $mz$-transportability, i.e., whether causal effects in the target domain are estimable from the information available. It further proves that a previously established algorithm for computing transport formula is in fact complete, that is, failure of the algorithm implies non-existence of a transport formula. Finally, the paper shows that the do-calculus is complete for the $mz$-transportability class.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
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Keyword Pioneer
— causal transportability
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Cross-Pollinator
— Artificial Intelligence, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization
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Hot Topic Early Bird
— causal inference