Exact Bayesian Structure Learning from Uncertain Interventions
Abstract
We show how to apply the dynamic programming algorithm of Koivisto and Sood [KS04, Koi06], which computes the exact posterior marginal edge probabilities $p(G_{ij} = 1 \mid D)$ of a DAG $G$ given data $D$, to the case where the data is obtained by interventions (experiments). In particular, we consider the case where the targets of the interventions are a priori unknown. We show that it is possible to learn the targets of intervention at the same time as learning the causal structure. We apply our exact technique to a biological data set that had previously been analyzed using MCMC [SPP+ 05, EW06, WGH06].
Cite
Text
Eaton and Murphy. "Exact Bayesian Structure Learning from Uncertain Interventions." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.Markdown
[Eaton and Murphy. "Exact Bayesian Structure Learning from Uncertain Interventions." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.](https://mlanthology.org/aistats/2007/eaton2007aistats-exact/)BibTeX
@inproceedings{eaton2007aistats-exact,
title = {{Exact Bayesian Structure Learning from Uncertain Interventions}},
author = {Eaton, Daniel and Murphy, Kevin},
booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics},
year = {2007},
pages = {107-114},
volume = {2},
url = {https://mlanthology.org/aistats/2007/eaton2007aistats-exact/}
}