Michael J. Daniels, Antonio Linero, Jason Roy

Bayesian Nonparametrics for Causal Inference and Missing Data (eBook, PDF)

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 262
• Erscheinungstermin: 23. August 2023
• Englisch
• ISBN-13: 9781000927719
• Artikelnr.: 68372386

Autorenporträt

Dr. Daniels received his undergraduate degree from Brown University in Applied Mathematics and doctoral degree from Harvard University in Biostatistics. He has been on the faculty at Iowa State and University of Texas at Austin.

Currently, Dr. Daniels is Professor, Andrew Banks Family Endowed Chair, and Chair in the Department of Statistics at the University of Florida. He is a past president of ENAR. He is a fellow of the American Statistical Association, past chair of the Statistics in Epidemiology Section of the American Statistical Association (ASA), former chair of the Biometrics Section of the ASA, and former editor of Biometrics.

He has received the Lagakos Distinguished Alumni Award from Harvard Biostatistics and the L. Adrienne Cupples Award from Boston University.

He has published extensively on Bayesian methods for missing data, longitudinal data and causal inference and has been funded by NIH R01 grants as PI and/or MPI since 2001. He also has a strong and productive record of collaborative research, with a focus on behavioral trials in smoking cessation and weight management, muscular dystrophy, and HIV.

Dr. Linero received his PhD in Statistics from the University of Florida. He is currently Assistant Professor in the Department of Statistics and Data Sciences at the University of Texas at Austin. His research is broadly focused on developing flexible Bayesian methods for complex longitudinal data, as well as developing tools for model selection, variable selection, and causal inference within the Bayesian nonparametric framework for high-dimensional problems.

Dr. Roy received his PhD in Biostatistics from the University of Michigan. He is currently Professor of Biostatistics and Chair of the Department of Biostatistics and Epidemiology at Rutgers School of Public Health. He directs the biostatistics core of the New Jersey Alliance for Clinical and Translational Science. He is a fellow of the American Statistical Association (ASA) and recipient of the Causality in Statistics Education Award from the ASA. His methodological research has focused on flexible Bayesian methods for causal inference. As a collaborative statistician, he has worked on studies in many areas of medicine and public health, including chronic kidney disease, hepatotoxicity of medications, and SARS-CoV-2.

Inhaltsangabe

Part I. Overview of Bayesian inference in causal inference and missing data and identifiability. 1. Overview of causal inference. 2. Overview of missing data. 3. Overview of Bayesian Inference for Missing Data and Causal Inference. Part II. Bayesian nonparametrics for causal inference and missing data. 4. Identifiability and Sensitivity Analysis. 5. Bayesian Decision Trees and their Ensembles. Part III. Identification and sensitivity analysis. 6. Dirichlet Process Mixtures and extensions. 7. Gaussian process prior and Dependent Dirichlet processes. 8. Causal Inference on Quantiles using Propensity scores. 9. Causal Inference with a point treatment using an EDPM model. 10. DDP+GP for causal inference using marginal structural models. 11. DPMs for Dropout in Longitudinal Studies. 12. DPMs for Non-Monotone Missingness.