Causal statistical inference in high dimensions

DOI10.1007/s00186-012-0404-7zbMath1339.62001OpenAlexW2008510080WikidataQ130552210 ScholiaQ130552210MaRDI QIDQ2392815

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Publication date: 2 August 2013

Published in: Mathematical Methods of Operations Research (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/20.500.11850/69992

zbMATH Keywords

directed acyclic graphsobservational datagraphical modelingPC-algorithmintervention calculus (do-operator)

Mathematics Subject Classification ID

Multivariate analysis (62H99) Measures of association (correlation, canonical correlation, etc.) (62H20) Research exposition (monographs, survey articles) pertaining to statistics (62-02)

Related Items (4)

Marginal integration for nonparametric causal inference ⋮ Inferring large graphs using \(\ell_1\)-penalized likelihood ⋮ Bayesian networks and the assessment of universities' value added ⋮ Causal inference in partially linear structural equation models

Uses Software

• pcalg
• glasso
• TETRAD

Cites Work

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• Variable selection in high-dimensional linear models: partially faithful distributions and the PC-simple algorithm
• Sparse inverse covariance estimation with the graphical lasso
• \(\ell_{0}\)-penalized maximum likelihood for sparse directed acyclic graphs
• Estimating high-dimensional intervention effects from observational data
• Causation, prediction, and search
• A characterization of Markov equivalence classes for acyclic digraphs
• High-dimensional graphs and variable selection with the Lasso
• Stability Selection
• 10.1162/153244303321897717
• Regularization and Variable Selection Via the Elastic Net
• Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs

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