# Improving the reliability of causal discovery from small data sets using argumentation

Título | Improving the reliability of causal discovery from small data sets using argumentation |

Publication Type | Journal Article |

Year of Publication | 2009 |

Authors | Bromberg F, Margaritis D |

Journal | The Journal of Machine Learning Research |

Volume | 10 |

Pagination | 301–340 |

Date Published | 02/2009 |

Palabras clave | argumentation, causal Bayesian netw orks, Independence-based causal discovery, reliability improvement, Structure learning |

Abstract | We address the problem of improving the reliability of independence-based causal discovery algorithms that results from the execution of statistical independence tests on small data sets, which typically have low reliability. We model the problem as a knowledge base containing a set of independence facts that are related through Pearl's well-known axioms. Statistical tests on finite data sets may result in errors in these tests and inconsistencies in the knowledge base. We resolve these inconsistencies through the use of an instance of the class of defeasible logics called argumentation, augmented with a preference function, that is used to reason about and possibly correct errors in these tests. This results in a more robust conditional independence test, called an |

URL | http://www.jmlr.org/papers/v10/bromberg09a.html |