Jointly Interventional and Observational Data: Estimation of Interventional Markov Equivalence Classes of Directed Acyclic Graphs

DOI10.1111/rssb.12071zbMath1414.62021arXiv1303.3216OpenAlexW2138997118MaRDI QIDQ5379910

Publication date: 14 June 2019

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1303.3216

zbMATH Keywords

maximum likelihood estimation Bayesian information criterion interventions causal inference graphical model greedy equivalence search

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Bayesian inference (62F15) Graphical methods in statistics (62A09)

Related Items (14)

Learning Markov equivalence classes of directed acyclic graphs: an objective Bayes approach ⋮ Objective Bayesian Nets for Integrating Consistent Datasets ⋮ Bayesian inference of causal effects from observational data in Gaussian graphical models ⋮ Network Structure Learning Under Uncertain Interventions ⋮ High-dimensional joint estimation of multiple directed Gaussian graphical models ⋮ Inferring large graphs using \(\ell_1\)-penalized likelihood ⋮ Compatible priors for model selection of high-dimensional Gaussian DAGs ⋮ Invariance, causality and robustness ⋮ Equivalence class selection of categorical graphical models ⋮ Network modeling in biology: statistical methods for gene and brain networks ⋮ Penalized Estimation of Directed Acyclic Graphs From Discrete Data ⋮ Objective Bayes model selection of Gaussian interventional essential graphs for the identification of signaling pathways ⋮ Beyond the mean: a flexible framework for studying causal effects using linear models ⋮ Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo

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