Probabilistic Evaluation of Sequential Plans from Causal Models with Hidden Variables
Artificial Intelligence
2013-02-21 v1
Abstract
The paper concerns the probabilistic evaluation of plans in the presence of unmeasured variables, each plan consisting of several concurrent or sequential actions. We establish a graphical criterion for recognizing when the effects of a given plan can be predicted from passive observations on measured variables only. When the criterion is satisfied, a closed-form expression is provided for the probability that the plan will achieve a specified goal.
Cite
@article{arxiv.1302.4977,
title = {Probabilistic Evaluation of Sequential Plans from Causal Models with Hidden Variables},
author = {Judea Pearl and James M. Robins},
journal= {arXiv preprint arXiv:1302.4977},
year = {2013}
}
Comments
Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)