Bayesian Nonparametrics For Causal Inference

Description: Please refer to the section BELOW (and NOT ABOVE) this line for the product details - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Title:Bayesian Nonparametrics For Causal InferenceISBN13:9780367341008ISBN10:036734100XAuthor:Daniels, Michael J. (Author), Linero, Antonio (Author), Roy, Jason (Author)Description:Bayesian Nonparametric Methods For Missing Data And Causal Inference Provides An Overview Of Flexible Bayesian Nonparametric (Bnp) Methods For Modeling Joint Or Conditional Distributions And Functional Relationships, And Their Interplay With Causal Inference And Missing Data This Book Emphasizes The Importance Of Making Untestable Assumptions To Identify Estimands Of Interest, Such As Missing At Random Assumption For Missing Data And Unconfoundedness For Causal Inference In Observational Studies The Bnp Approach Can Account For Possible Violations Of Assumptions And Minimize Concerns About Model Misspecification, Unlike Parametric Methods The Overall Strategy Is To First Specify Bnp Models For Observed Data And Second To Specify Additional Uncheckable Assumptions To Identify Estimands Of Interest The Book Is Divided Into Three Parts Part I Develops The Key Concepts In Causal Inference And Missing Data, And Reviews Relevant Concepts In Bayesian Inference Part Ii Introduces The Fundamental Bnp Tools Required To Address Causal Inference And Missing Data Problems Part Iii Shows How The Bnp Approach Can Be Applied In A Variety Of Case Studies The Datasets In The Case Studies Come From Electronic Health Records Data, Survey Data, Cohort Studies, And Randomized Clinical Trials Features: - Thorough Discussion Of Both Bnp And Its Interplay With Causal Inference And Missing Data- How To Use Bnp And G-Computation For Causal Inference And Nonignorable Missingness- How To Derive And Calibrate Sensitivity Parameters To Assess Sensitivity To Deviations From Uncheckable Causal Andor Missingness Assumptions- Detailed Case Studies Illustrating The Application Of Bnp Methods To Causal Inference And Missing Data- R-Code Andor Packages To Implement Bnp In Causal Inference And Missing Data Problemsthe Book Is Primarily Aimed At Researchers And Graduate Students From Statistics And Biostatistics It Will Also Serve As A Useful Practical Reference For Mathematically-Sophisticated Epidemiologists And Medical Researchers Binding:Hardcover, HardcoverPublisher:CRC PressPublication Date:2023-10-30Weight:0.99 lbsDimensions:Number of Pages:252Language:English

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Location: USA

End Time: 2024-11-03T17:53:58.000Z

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