Transportability of Causal and Statistical Relations: A Formal Approach
Abstract
We address the problem of transferring information learned from experiments to a different environment, in which only passive observations can be collected. We introduce a formal representation called "selection diagrams" for expressing knowledge about differences and commonalities between environments and, using this representation, we derive procedures for deciding whether effects in the target environment can be inferred from experiments conducted elsewhere. When the answer is affirmative, the procedures identify the set of experiments and observations that need be conducted to license the transport. We further discuss how transportability analysis can guide the transfer of knowledge in non-experimental learning to minimize re-measurement cost and improve prediction power.
Cite
Text
Pearl and Bareinboim. "Transportability of Causal and Statistical Relations: A Formal Approach." AAAI Conference on Artificial Intelligence, 2011. doi:10.1609/AAAI.V25I1.7861Markdown
[Pearl and Bareinboim. "Transportability of Causal and Statistical Relations: A Formal Approach." AAAI Conference on Artificial Intelligence, 2011.](https://mlanthology.org/aaai/2011/pearl2011aaai-transportability/) doi:10.1609/AAAI.V25I1.7861BibTeX
@inproceedings{pearl2011aaai-transportability,
title = {{Transportability of Causal and Statistical Relations: A Formal Approach}},
author = {Pearl, Judea and Bareinboim, Elias},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2011},
pages = {247-254},
doi = {10.1609/AAAI.V25I1.7861},
url = {https://mlanthology.org/aaai/2011/pearl2011aaai-transportability/}
}