Improving the Reliability of Causal Discovery from Small Data Sets using Argumentation

FACUNDO BROMBERG; DIMITRIS MARGARITIS

JOURNAL OF MACHINE LEARNING RESEARCH

Lugar: online; Año: 2009 vol. 10 p. 301 - 340

1533-7928

 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 the well-known Pearl´s 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 argumentative independence test. Our experimental evaluation shows clear positive improvements in the accuracy of argumentative over purely statistical tests. We also demonstrate significant improvements on the accuracy of causal structure discovery from the outcomes of independence tests on both data sampled from randomly generated causal models and real-world data sets.