Bayesian nonparametrics for causal inference and missing data
著者
書誌事項
Bayesian nonparametrics for causal inference and missing data
（Monographs on statistics and applied probability, 173）（A Chapman & Hall book）
CRC Press, 2024
- : hbk
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
* 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 and/or missingness assumptions
* Detailed case studies illustrating the application of BNP methods to causal inference and missing data
* R-code and/or packages to implement BNP in causal inference and missing data problems
目次
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.
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