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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...