Meta-Transportability of Causal Effects: A Formal Approach

Elias Bareinboim, Judea Pearl
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, PMLR 31:135-143, 2013.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v31-bareinboim13a, title = {Meta-Transportability of Causal Effects: A Formal Approach}, author = {Bareinboim, Elias and Pearl, Judea}, booktitle = {Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics}, pages = {135--143}, year = {2013}, editor = {Carvalho, Carlos M. and Ravikumar, Pradeep}, volume = {31}, series = {Proceedings of Machine Learning Research}, address = {Scottsdale, Arizona, USA}, month = {29 Apr--01 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v31/bareinboim13a.pdf}, url = {https://proceedings.mlr.press/v31/bareinboim13a.html}, abstract = {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. } }
Endnote
%0 Conference Paper %T Meta-Transportability of Causal Effects: A Formal Approach %A Elias Bareinboim %A Judea Pearl %B Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2013 %E Carlos M. Carvalho %E Pradeep Ravikumar %F pmlr-v31-bareinboim13a %I PMLR %P 135--143 %U https://proceedings.mlr.press/v31/bareinboim13a.html %V 31 %X 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.
RIS
TY - CPAPER TI - Meta-Transportability of Causal Effects: A Formal Approach AU - Elias Bareinboim AU - Judea Pearl BT - Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics DA - 2013/04/29 ED - Carlos M. Carvalho ED - Pradeep Ravikumar ID - pmlr-v31-bareinboim13a PB - PMLR DP - Proceedings of Machine Learning Research VL - 31 SP - 135 EP - 143 L1 - http://proceedings.mlr.press/v31/bareinboim13a.pdf UR - https://proceedings.mlr.press/v31/bareinboim13a.html AB - 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. ER -
APA
Bareinboim, E. & Pearl, J.. (2013). Meta-Transportability of Causal Effects: A Formal Approach. Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 31:135-143 Available from https://proceedings.mlr.press/v31/bareinboim13a.html.

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