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Causal Inference in R

You're reading from Causal Inference in R Decipher complex relationships with advanced R techniques for data-driven decision-making

Product type Paperback

Published in Nov 2024

Publisher Packt

ISBN-13 9781837639021

Length 382 pages

Edition 1st Edition

Languages

R

Concepts

Data Science

Author (1):

Subhajit Das

View More author details

Table of Contents (21) Chapters

Preface

1. Part 1:Foundations of Causal Inference

2. Chapter 1: Introducing Causal Inference FREE CHAPTER

3. Chapter 2: Unraveling Confounding and Associations

4. Chapter 3: Initiating R with a Basic Causal Inference Example

5. Part 2: Practical Applications and Core Methods

6. Chapter 4: Constructing Causality Models with Graphs

7. Chapter 5: Navigating Causal Inference through Directed Acyclic Graphs

8. Chapter 6: Employing Propensity Score Techniques

9. Chapter 7: Employing Regression Approaches for Causal Inference

10. Chapter 8: Executing A/B Testing and Controlled Experiments

11. Chapter 9: Implementing Doubly Robust Estimation

12. Part 3: Advanced Topics and Cutting-Edge Methods

13. Chapter 10: Analyzing Instrumental Variables

14. Chapter 11: Investigating Mediation Analysis

15. Chapter 12: Exploring Sensitivity Analysis

16. Chapter 13: Scrutinizing Heterogeneity in Causal Inference

17. Chapter 14: Harnessing Causal Forests and Machine Learning Methods

18. Chapter 15: Implementing Causal Discovery in R

19. Index

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20. Other Books You May Enjoy

Technical requirements

You can find the code examples for this chapter in this book’s GitHub repository: https://github.com/PacktPublishing/Causal-Inference-in-R/tree/main/chap_13.

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Authors (1)

Subhajit Das

Subhajit Das

Subhajit Das holds a PhD in computer science from Georgia Institute of Technology, USA, specializing in machine learning (ML) and visual analytics. With 10+ years of experience, he is an expert in causal inference, revealing complex relationships and data-driven decision-making. His work has influenced millions in AI, e-commerce, logistics, and 3D software sectors. He has collaborated with leading companies, such as Amazon, Microsoft, Bosch, UPS, 3M, and Autodesk, creating solutions that seamlessly integrate causal reasoning and ML. His research, published in top conferences, focuses on developing AI-powered interactive systems for domain experts. He also holds a master's degree in design computing from the University of Pennsylvania, USA.

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