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### 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.

Original language | English |
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Pages (from-to) | 1011-1027 |

Number of pages | 17 |

Journal | Synthese |

Volume | 193 |

Issue number | 4 |

Early online date | 11 Feb 2015 |

DOIs | |

Publication status | Published - Apr 2016 |

### Keywords

- Bayes nets
- Causal inference
- Faithfulness
- Graphical models

## Fingerprint Dive into the research topics of 'The three faces of faithfulness'. Together they form a unique fingerprint.

## Projects

- 1 Finished

## Philosophical Implications of Recent Advances in Causal Modeling (因果建模和推理的新方法的哲學探究)

ZHANG, J. & ZHANG, K.

1/08/13 → 31/01/16

Project: Grant Research

## Cite this

ZHANG, J., & SPIRTES, P. (2016). The three faces of faithfulness.

*Synthese*,*193*(4), 1011-1027. https://doi.org/10.1007/s11229-015-0673-9