Bayesian inference of causal effects from observational data in Gaussian graphical models.
Markov equivalence class
causal inference
directed acyclic graph
graphical model
objective Bayes
observational data
Journal
Biometrics
ISSN: 1541-0420
Titre abrégé: Biometrics
Pays: United States
ID NLM: 0370625
Informations de publication
Date de publication:
03 2021
03 2021
Historique:
received:
16
10
2019
revised:
25
03
2020
accepted:
30
03
2020
pubmed:
16
4
2020
medline:
26
10
2021
entrez:
16
4
2020
Statut:
ppublish
Résumé
We assume that multivariate observational data are generated from a distribution whose conditional independencies are encoded in a Directed Acyclic Graph (DAG). For any given DAG, the causal effect of a variable onto another one can be evaluated through intervention calculus. A DAG is typically not identifiable from observational data alone. However, its Markov equivalence class (a collection of DAGs) can be estimated from the data. As a consequence, for the same intervention a set of causal effects, one for each DAG in the equivalence class, can be evaluated. In this paper, we propose a fully Bayesian methodology to make inference on the causal effects of any intervention in the system. Main features of our method are: (a) both uncertainty on the equivalence class and the causal effects are jointly modeled; (b) priors on the parameters of the modified Cholesky decomposition of the precision matrices across all DAG models are constructively assigned starting from a unique prior on the complete (unrestricted) DAG; (c) an efficient algorithm to sample from the posterior distribution on graph space is adopted; (d) an objective Bayes approach, requiring virtually no user specification, is used throughout. We demonstrate the merits of our methodology in simulation studies, wherein comparisons with current state-of-the-art procedures turn out to be highly satisfactory. Finally we examine a real data set of gene expressions for Arabidopsis thaliana.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
136-149Subventions
Organisme : Università Cattolica del Sacro Cuore
ID : D.3.2
Organisme : Università Cattolica del Sacro Cuore
ID : D1
Informations de copyright
© 2020 The International Biometric Society.
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