Causality, Constraints and Indirect Effects
Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence. Diagnosis and Qualitative Reasoning. Pages 555-560.
Abstract
The goal of this paper is the formulation of a well-founded solution to the ramification problem: the problem of determining the indirect effects of an action. The solution relies on a model of causal reasoning based on two simple ideas: that the values that a variable may take are a function of the values of its causes, and that the form of that function is determined by the rules acting upon the variable. The model is related to models of causal reasoning based on Bayesian Networks and Structural Equation Models but does not appeal to probabilities or rankings of any sort. It also combines (propositional) action rules and causal rules in a clear manner, and provides a simple way for determining when 'derived information' can be assumed to persist.
@inproceedings{ijcai1997p82,
title = {Causality, Constraints and Indirect Effects},
author = {Geffner, Hector},
booktitle = {Proceedings of the Fifteenth International Joint Conference on
Artificial Intelligence, {IJCAI-97}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Martha E. Pollack},
pages = {555--560},
year = {1997},
note = {Diagnosis and Qualitative Reasoning},
url = {https://ijcai.org/proceedings/1997/82},
}
TY - CPAPER TI - Causality, Constraints and Indirect Effects AU - Geffner, Hector T2 - Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, IJCAI-97 PB - International Joint Conferences on Artificial Intelligence PY - 1997 SP - 555 EP - 560 UR - https://ijcai.org/proceedings/1997/82 ER -Copied
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