A GENERALIZED BACK-DOOR CRITERION

• 成果类型: Article
• 署名作者: Maathuis, Marloes H.; Colombo, Diego
• 署名单位: Swiss Federal Institutes of Technology Domain; ETH Zurich
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/14-AOS1295
• 发表日期: 2015
• 页码: 1060-1088
• 关键词: directed acyclic graphs; equivalence classes; markov equivalence; Causal Inference; selection; latent

摘要

We generalize Pearl's back-door criterion for directed acyclic graphs (DAGs) to more general types of graphs that describe Markov equivalence classes of DAGs and/or allow for arbitrarily many hidden variables. We also give easily checkable necessary and sufficient graphical criteria for the existence of a set of variables that satisfies our generalized back-door criterion, when considering a single intervention and a single outcome variable. Moreover, if such a set exists, we provide an explicit set that fulfills the criterion. We illustrate the results in several examples. R-code is available in the R-package pcalg.