Bayesian Nonparametrics for Causal Inference and Missing Data

Explore the innovative world of Bayesian Nonparametrics with "Bayesian Nonparametrics for Causal Inference and Missing Data" by Michael J. Daniels. Published by Taylor & Francis Ltd in 2023, this hardback edition spans 248 pages and delves into the flexible modeling of joint and conditional distributions, along with functional relationships. The book expertly combines Bayesian nonparametric methods with causal and missingness assumptions, providing readers with the tools to utilize the g-formula for inferring causal effects. Perfect for researchers and practitioners alike, this comprehensive guide serves as an essential resource for anyone looking to deepen their understanding of causal inference and missing data analysis. Enhance your statistical toolkit and gain insights into advanced methodologies with this indispensable work by Michael J. Daniels.