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Title: Causal models with constraints
Causal models have proven extremely useful in offering formal representations of causal relationships between a set of variables. Yet in many situations, there are non-causal relationships among variables. For example, we may want variables LDL, HDL, and TOT that represent the level of low-density lipoprotein cholesterol, the level of lipoprotein high-density lipoprotein cholesterol, and total cholesterol level, with the relation LDL+HDL=\OT. This cannot be done in standard causal models, because we can intervene simultaneously on all three variables. The goal of this paper is to extend standard causal models to allow for constraints on settings of variables. Although the extension is relatively straightforward, to make it useful we have to define a new intervention operation that disconnects a variable from a causal equation. We give examples showing the usefulness of this extension, and provide a sound and complete axiomatization for causal models with constraints.  more » « less
Award ID(s):
1703846
PAR ID:
10414207
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the Second Conference on Causal Learning and Reasoning
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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