## Improving the Reliability of Causal Discovery from Small Data Sets Using Argumentation

** Facundo Bromberg, Dimitris Margaritis**; 10(12):301−340, 2009.

### 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 *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
both on sampled data from randomly generated causal models and on
real-world data sets.

© JMLR 2009. (edit, beta) |