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Chapman and Hall/CRC Bayesian Nonparametrics for Causal Inference - CRC Books

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Description

Explore flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships in this Chapman and Hall/CRC monograph. This text examines how BNP approaches work with causal inference and missing data. Unlike standard parametric methods, these techniques help account for possible violations of assumptions. The book addresses the necessity of making untestable assumptions to identify estimands, such as unconfoundedness for causal inference in observational studies and the missing at random assumption for missing data.

Key Features

This Chapman and Hall/CRC monograph provides an overview of flexible Bayesian nonparametric methods for modeling distributions.

Learn how to apply BNP approaches to handle the relationship between causal inference and missing data.

The text covers modeling functional relationships and joint or conditional distributions using nonparametric methods.

Understand how to manage untestable assumptions like unconfoundedness for causal inference in observational studies.

Use these methods to account for possible violations of assumptions that often affect parametric models.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
23 August 2023
Listed Since
22 February 2023

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