Transportability of Causal Effects: Completeness Results

Authors

  • Elias Bareinboim University of California, Los Angeles
  • Judea Pearl University of California, Los Angeles

DOI:

https://doi.org/10.1609/aaai.v26i1.8232

Abstract

The study of transportability aims to identify conditions under which causal information learned from experiments can be reused in a different environment where only passive observations can be collected. The theory introduced in [Pearl and Bareinboim, 2011] (henceforth [PB, 2011]) defines formal conditions for such transfer but falls short of providing an effective procedure for deciding, given assumptions about differences between the source and target domains, whether transportability is feasible. This paper provides such procedure. It establishes a necessary and sufficient condition for deciding when causal effects in the target domain are estimable from both the statistical information available and the causal information transferred from the experiments. The paper further provides a complete algorithm for computing the transport formula, that is, a way of fusing experimental and observational information to synthesize an estimate of the desired causal relation.

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Published

2021-09-20

How to Cite

Bareinboim, E., & Pearl, J. (2021). Transportability of Causal Effects: Completeness Results. Proceedings of the AAAI Conference on Artificial Intelligence, 26(1), 698-704. https://doi.org/10.1609/aaai.v26i1.8232

Issue

Section

AAAI Technical Track: Knowledge Representation and Reasoning