Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs

• 成果类型: Article
• 署名作者: Hauser, Alain; Buehlmann, Peter
• 署名单位: University of Bern; Swiss Institute of Bioinformatics; Swiss Federal Institutes of Technology Domain; ETH Zurich
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12071
• 发表日期: 2015
• 页码: 291-318
• 关键词: CAUSAL; MODEL; networks

摘要

In many applications we have both observational and (randomized) interventional data. We propose a Gaussian likelihood framework for joint modelling of such different data types, based on global parameters consisting of a directed acyclic graph and corresponding edge weights and error variances. Thanks to the global nature of the parameters, maximum likelihood estimation is reasonable with only one or few data points per intervention. We prove consistency of the Bayesian information criterion for estimating the interventional Markov equivalence class of directed acyclic graphs which is smaller than the observational analogue owing to increased partial identifiability from interventional data. Such an improvement in identifiability has immediate implications for tighter bounds for inferring causal effects. Besides methodology and theoretical derivations, we present empirical results from real and simulated data.