Bayesian nonparametric methods for causal inference and missing data

Bayesian nonparametric methods for causal inference and missing data

"Bayesian nonparametric (BNP) methods can be used to flexibly model joint or conditional distributions, as well as functional relationships. These methods, along with causal and/or missingness assumptions, can be used with the g-formula to infer causal effects"--

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Bibliographische Detailangaben
1. Verfasser: Daniels, Michael J. (VerfasserIn)
Weitere Verfasser: Linero, Antonio (VerfasserIn), Roy, Jason (VerfasserIn)
Format: UnknownFormat
Sprache:eng
Veröffentlicht: Boca Raton, London, New York CRC Press 2024
Ausgabe:First edition
Schriftenreihe:Monographs on statistics and applied probability 173
Schlagworte:
Nonparametric statistics > Bayesian statistical decision theory > Missing observations (Statistics) > Bayes-Inferenz > Fehlende Daten > Kausalmodell > Nichtparametrische Regression > Sensitivitätsanalyse > Entscheidungsbaum > Medizinische Statistik
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Beschreibung
Zusammenfassung:"Bayesian nonparametric (BNP) methods can be used to flexibly model joint or conditional distributions, as well as functional relationships. These methods, along with causal and/or missingness assumptions, can be used with the g-formula to infer causal effects"--
Beschreibung:Literaturverzeichnis: Seite 229-241
Beschreibung:xiv, 248 Seiten
Illustrationen, Diagramme
ISBN:9780367341008
978-0-367-34100-8
9781032456942
978-1-032-45694-2