GEOMETRY OF THE FAITHFULNESS ASSUMPTION IN CAUSAL INFERENCE
成果类型：
Article
署名作者：
Uhler, Caroline; Raskutti, Garvesh; Buehlmann, Peter; Yu, Bin
署名单位：
Institute of Science & Technology - Austria; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of California System; University of California Berkeley
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
DOI：
10.1214/12-AOS1080
发表日期：
2013
页码：
436-463
关键词：
摘要：
Many algorithms for inferring causality rely heavily on the faithfulness assumption. The main justification for imposing this assumption is that the set of unfaithful distributions has Lebesgue measure zero, since it can be seen as a collection of hypersurfaces in a hypercube. However, due to sampling error the faithfulness condition alone is not sufficient for statistical estimation, and strong-faithfulness has been proposed and assumed to achieve uniform or high-dimensional consistency. In contrast to the plain faithfulness assumption, the set of distributions that is not strong-faithful has nonzero Lebesgue measure and in fact, can be surprisingly large as we show in this paper. We study the strong-faithfulness condition from a geometric and combinatorial point of view and give upper and lower bounds on the Lebesgue measure of strong-faithful distributions for various classes of directed acyclic graphs. Our results imply fundamental limitations for the PC-algorithm and potentially also for other algorithms based on partial correlation testing in the Gaussian case.