A framework for Bayesian nonparametric inference for causal effects of mediation

DOI10.1111/biom.12575zbMath1372.62073OpenAlexW2491916876WikidataQ39537869 ScholiaQ39537869MaRDI QIDQ5283294

Chanmin Kim, Bess H. Marcus, Jason A. Roy, Michael J. Daniels

Publication date: 21 July 2017

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc5288310

zbMATH Keywords

sensitivity analysiscausal inferenceDirichlet process mixturemultivariate normalsBayesian nonparametric (BNP) framework

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Bayesian inference (62F15)

Related Items (11)

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Uses Software

• SPSS
• mediation
• SAS
• DPpackage

Cites Work

• Semiparametric theory for causal mediation analysis: efficiency bounds, multiple robustness and sensitivity analysis
• Identification, inference and sensitivity analysis for causal mediation effects
• An introduction to copulas. Properties and applications
• Exploiting multiple outcomes in Bayesian principal stratification analysis with application to the evaluation of a job training program
• Generalized Causal Mediation Analysis
• A Bayesian Semiparametric Approach to Intermediate Variables in Causal Inference
• Bayesian curve fitting using multivariate normal mixtures
• Ordinal Data Modeling
• Bayesian Density Estimation and Inference Using Mixtures
• Bayesian Inference for the Causal Effect of Mediation

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