Cyclic Causal Models with Discrete Variables: Markov Chain Equilibrium Semantics and Sample Ordering
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 1060-1067.
Abstract
We analyze the foundations of cyclic causal models for discrete variables, and compare structural equation models (SEMs) to an alternative semantics as the equilibrium (stationary) distribution of a Markov chain. We show under general conditions, discrete cyclic SEMs cannot have independent noise; even in the simplest case, cyclic structural equation models imply constraints on the noise. We give a formalization of an alternative Markov chain equilibrium semantics which requires not only the causal graph, but also a sample order. We show how the resulting equilibrium is a function of the sample ordering, both theoretically and empirically.
@inproceedings{ijcai2013p161,
title = {Cyclic Causal Models with Discrete Variables: Markov Chain Equilibrium Semantics and Sample Ordering},
author = {Poole, David and Crowley, Mark},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {1060--1067},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/161},
}
TY - CPAPER TI - Cyclic Causal Models with Discrete Variables: Markov Chain Equilibrium Semantics and Sample Ordering AU - Poole, David AU - Crowley, Mark T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 1060 EP - 1067 UR - https://ijcai.org/proceedings/2013/161 ER -Copied
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