Testing Identifiability of Causal Effects
Abstract
This paper concerns the probabilistic evaluation of the effects of actions in the presence of unmeasured variables. We show that the identification of causal effect between a singleton variable X and a set of variables Y can be accomplished systematically, in time polynomial in the number of variables in the graph. When the causal effect is identifiable, a closed-form expression can be obtained for the probability that the action will achieve a specified goal, or a set of goals.
Cite
Text
Galles and Pearl. "Testing Identifiability of Causal Effects." Conference on Uncertainty in Artificial Intelligence, 1995.Markdown
[Galles and Pearl. "Testing Identifiability of Causal Effects." Conference on Uncertainty in Artificial Intelligence, 1995.](https://mlanthology.org/uai/1995/galles1995uai-testing/)BibTeX
@inproceedings{galles1995uai-testing,
title = {{Testing Identifiability of Causal Effects}},
author = {Galles, David and Pearl, Judea},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1995},
pages = {185-195},
url = {https://mlanthology.org/uai/1995/galles1995uai-testing/}
}