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The inference of causal relationships using observational data from partially observed multivariate systems with hidden variables is a fundamental question in many scientific domains.
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Comparison of strategies for scalable causal discovery of latent variable models from mixed data
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ABCD-strategy: Budgeted experimental design for targeted causal structure discovery
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Conditionally-additive-noise models for structure learning
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Review of causal discovery methods based on graphical models
C. Glymour, K. Zhang, and P. Spirtes · 2019
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The evaluation of discovery: Models, simulation and search through “big data”
J. D. Ramsey, K. Zhang, and C. Glymour · 2019
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Advancing functional connectivity research from association to causation
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Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods
R. Sanchez-Romero, J. D. Ramsey, K. Zhang, M. R. K. Glymour, B. Huang, and C. Glymour · 2019
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