A Characterization of Interventional Distributions in Semi-Markovian Causal Models
Abstract
We offer a complete characterization of the set of distributions that could be induced by local interventions on variables governed by a causal Bayesian network of unknown structure, in which some of the variables remain unmeasured. We show that such distributions are constrained by a simply formulated set of inequalities, from which bounds can be derived on causal effects that are not directly measured in randomized experiments.
Cite
Text
Tian et al. "A Characterization of Interventional Distributions in Semi-Markovian Causal Models." AAAI Conference on Artificial Intelligence, 2006.Markdown
[Tian et al. "A Characterization of Interventional Distributions in Semi-Markovian Causal Models." AAAI Conference on Artificial Intelligence, 2006.](https://mlanthology.org/aaai/2006/tian2006aaai-characterization/)BibTeX
@inproceedings{tian2006aaai-characterization,
title = {{A Characterization of Interventional Distributions in Semi-Markovian Causal Models}},
author = {Tian, Jin and Kang, Changsung and Pearl, Judea},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2006},
pages = {1239-1244},
url = {https://mlanthology.org/aaai/2006/tian2006aaai-characterization/}
}