Causal Inference by Surrogate Experiments: Z-Identifiability
Abstract
We address the problem of estimating the effect of intervening on a set of variables X from experiments on a different set, Z, that is more accessible to manipulation. This problem, which we call z-identifiability, reduces to ordinary identifiability when Z = empty and, like the latter, can be given syntactic characterization using the do-calculus [Pearl, 1995; 2000]. We provide a graphical necessary and sufficient condition for z-identifiability for arbitrary sets X,Z, and Y (the outcomes). We further develop a complete algorithm for computing the causal effect of X on Y using information provided by experiments on Z. Finally, we use our results to prove completeness of do-calculus relative to z-identifiability, a result that does not follow from completeness relative to ordinary identifiability.
Cite
Text
Bareinboim and Pearl. "Causal Inference by Surrogate Experiments: Z-Identifiability." Conference on Uncertainty in Artificial Intelligence, 2012.Markdown
[Bareinboim and Pearl. "Causal Inference by Surrogate Experiments: Z-Identifiability." Conference on Uncertainty in Artificial Intelligence, 2012.](https://mlanthology.org/uai/2012/bareinboim2012uai-causal/)BibTeX
@inproceedings{bareinboim2012uai-causal,
title = {{Causal Inference by Surrogate Experiments: Z-Identifiability}},
author = {Bareinboim, Elias and Pearl, Judea},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2012},
pages = {113-120},
url = {https://mlanthology.org/uai/2012/bareinboim2012uai-causal/}
}