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• 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
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