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Transportability from multiple environments with limited experiments

Cited 0 time in Web of Science Cited 13 time in Scopus
Authors

Bareinboim, Elias; Lee, Sanghack; Honavar, Vasant; Pearl, Judea

Issue Date
2013
Publisher
Neural information processing systems foundation
Citation
Advances in Neural Information Processing Systems
Abstract
This paper considers the problem of transferring experimental findings learned from multiple heterogeneous domains to a target domain, in which only limited experiments can be performed. We reduce questions of transportability from multiple domains and with limited scope to symbolic derivations in the causal calculus, thus extending the original setting of transportability introduced in [1], which assumes only one domain with full experimental information available. We further provide different graphical and algorithmic conditions for computing the transport formula in this setting, that is, a way of fusing the observational and experimental information scattered throughout different domains to synthesize a consistent estimate of the desired effects in the target domain. We also consider the issue of minimizing the variance of the produced estimand in order to increase power.
ISSN
1049-5258
URI
https://hdl.handle.net/10371/201571
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  • Graduate School of Data Science
Research Area Causal Decision Making, Causal Discovery, Causal Inference

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