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Real world observational data, together with causal inference, allow the estimation of causal effects when randomized controlled trials are not available.
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Michele Jonsson Funk, Daniel Westreich, Chris Wiesen, Til Stürmer, M Alan Brookhart, and Marie Davidian · 2011
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Isabelle Guyon, Constantin Aliferis, Gregory Cooper, André Elisseeff, Jean Philippe Pellet, Peter Spirtes, and Alexander Statnikov · 2011
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Assessing lack of common support in causal inference using bayesian nonparametrics: Implications for evaluating the effect of breastfeeding on children’s cognitive outcomes
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Causal inference in empirical archival financial accounting research
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M Sanni Ali, Rolf HH Groenwold, Svetlana V Belitser, Wiebe R Pestman, Arno W Hoes, Kit CB Roes, Anthonius de Boer, and Olaf H Klungel · 2015
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Miguel A Hernán, John Hsu, and Brian Healy · 2018
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Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data
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CohortMethod: New-user cohort method with large scale propensity and outcome models , 2018
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Association of hemoglobin a1c levels with use of sulfonylureas, dipeptidyl peptidase 4 inhibitors, and thiazolidinediones in patients with type 2 diabetes treated with metformin: analysis from the observational health data sciences and informatics initiative
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