Meta-Transportability of Causal Effects: A Formal Approach
In Proceedings of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 135-143, 2013.
9 Pages Posted: 26 Oct 2013
Date Written: July 22, 2013
This paper considers the problem of transferring experimental findings learned from multiple heterogeneous domains to a different environment, in which only passive observations can be collected. Pearl and Bareinboim (2011) established a complete characterization for such transfer between two domains, a source and a target, and this paper generalizes their results to multiple heterogeneous domains. It establishes a necessary and sufficient condition for deciding when effects in the target domain are estimable from both statistical and causal information transferred from the experiments in the source domains. The paper further provides a complete algorithm for computing the transport formula, that is, a way of fusing observational and experimental information to synthesize an unbiased estimate of the desired effects.
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