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Springer Demystifying Causal Inference - R Policy Guide

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Description

Demystifying Causal Inference: Public Policy Applications with R by Springer offers an accessible introduction to causal inference and data analysis. Designed specifically for a public policy audience, this book uses practical examples from various disciplines to explain complex topics. Readers gain a hands-on approach to working with data in R using the popular tidyverse package. The text covers high-quality R packages for specific causal inference techniques, including ggdag, Matching, rdrobust, and dosearch. The book is organized into two parts, starting with a detailed narrative about John Snow's historical investigations to provide context for modern data analysis applications.

Key Features

Provides an accessible introduction to causal inference and data analysis tailored for public policy professionals.

Utilizes a hands-on approach to data work in R through the use of the popular tidyverse package.

Includes practical policy examples from a range of disciplines to help explain complex concepts.

Covers specific causal inference techniques using high-quality R packages such as ggdag, Matching, rdrobust, and dosearch.

Features a two-part structure that begins with the historical investigations of John Snow to build foundational understanding.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
03 October 2024
Listed Since
04 October 2024

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