Title

The three faces of faithfulness

Document Type

Journal article

Source Publication

Synthese

Publication Date

4-2016

Volume

193

Issue

4

First Page

1011

Last Page

1027

Publisher

Springer Netherlands

Keywords

Causal inference, Bayes nets, Faithfulness, Graphical models

Abstract

In the causal inference framework of Spirtes, Glymour, and Scheines (SGS), inferences about causal relationships are made from samples from probability distributions and a number of assumptions relating causal relations to probability distributions. The most controversial of these assumptions is the Causal Faithfulness Assumption, which roughly states that if a conditional independence statement is true of a probability distribution generated by a causal structure, it is entailed by the causal structure and not just for particular parameter values. In this paper we show that the addition of the Causal Faithfulness Assumption plays three quite different roles in the SGS framework: (i) it reduces the degree of underdetermination of causal structure by probability distribution; (ii) computationally, it justifies reliable (constraint-based) causal inference algorithms that would otherwise have to be slower in order to be reliable; and (iii) statistically, it implies that those algorithms reliably obtain the correct answer at smaller sample sizes than would otherwise be the case. We also consider a number of variations on the Causal Faithfulness Assumption, and show how they affect each of these three roles.

DOI

10.1007/s11229-015-0673-9

Print ISSN

00397857

E-ISSN

15730964

Funding Information

The research of J. Zhang was supported in part by the Research Grants Council of Hong Kong under the General Research Fund LU342213.

Publisher Statement

Copyright © Springer Science+Business Media Dordrecht 2015.

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Recommended Citation

Zhang, J., & Spirtes, P. (2015). The three faces of faithfulness. Synthese. 193(4), 1011-1027. doi: 10.1007/s11229-015-0673-9