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Dormant independence

Dormant independence
Dormant independence
The construction of causal graphs from non-experimental data rests on a set of constraints that the graph structure imposes on all probability distributions compatible with the graph. These constraints are of two types: conditional inde pendencies and algebraic constraints, first noted by Verma. While conditional independencies are well studied and frequently used in causal induction algorithms, Verma constraints are still poorly understood, and rarely applied. In this paper we examine a special subset of Verma constraints which are easy to understand, easy to identify and easy to apply; they arise from “dormant independencies,” namely, conditional independencies that hold in interventional distributions. We give a complete algorithm for determining if a dormant independence between two sets of variables is entailed by the causal graph, such that this independence is identifiable, in other words if it resides in an interventional distribution that can be predicted without resorting to interventions. We further show the usefulness of dormant independencies in model testing and induction by giving an algorithm that uses constraints entailed by dormant independencies to prune
extraneous edges from a given causal graph.
9781577353683
1081-1087
AAAI Press
Shpitser, Ilya
4d295b9b-39e8-417f-b38d-fbb5d7df6992
Pearl, Judea
d4317e37-9d5f-4fdc-84ad-c7bf98f99476
Shpitser, Ilya
4d295b9b-39e8-417f-b38d-fbb5d7df6992
Pearl, Judea
d4317e37-9d5f-4fdc-84ad-c7bf98f99476

Shpitser, Ilya and Pearl, Judea (2008) Dormant independence. In Proceedings of the Twenty-third AAAI Conference on Artificial Intelligence. AAAI Press. pp. 1081-1087 .

Record type: Conference or Workshop Item (Paper)

Abstract

The construction of causal graphs from non-experimental data rests on a set of constraints that the graph structure imposes on all probability distributions compatible with the graph. These constraints are of two types: conditional inde pendencies and algebraic constraints, first noted by Verma. While conditional independencies are well studied and frequently used in causal induction algorithms, Verma constraints are still poorly understood, and rarely applied. In this paper we examine a special subset of Verma constraints which are easy to understand, easy to identify and easy to apply; they arise from “dormant independencies,” namely, conditional independencies that hold in interventional distributions. We give a complete algorithm for determining if a dormant independence between two sets of variables is entailed by the causal graph, such that this independence is identifiable, in other words if it resides in an interventional distribution that can be predicted without resorting to interventions. We further show the usefulness of dormant independencies in model testing and induction by giving an algorithm that uses constraints entailed by dormant independencies to prune
extraneous edges from a given causal graph.

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Published date: 2008
Organisations: Statistics

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Local EPrints ID: 350566
URI: http://eprints.soton.ac.uk/id/eprint/350566
ISBN: 9781577353683
PURE UUID: e5928a9e-1bb3-4f33-87f3-b8ebdd496088

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Date deposited: 04 Apr 2013 13:42
Last modified: 14 Mar 2024 13:27

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Contributors

Author: Ilya Shpitser
Author: Judea Pearl

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