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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

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Product type Paperback
Published in Nov 2024
Publisher Packt
ISBN-13 9781837639021
Length 382 pages
Edition 1st Edition
Languages
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Author (1):
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Subhajit Das Subhajit Das
Author Profile Icon Subhajit Das
Subhajit Das
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Table of Contents (21) Chapters Close

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 20. Other Books You May Enjoy

Basics of graph theory

When it comes to making sense of complex networks, there is graph theory, which contains nodes and edges as its bare bones. You might have seen a web of interconnected points – these are nodes or vertices, and they represent all sorts of things, depending on what you’re looking at. Now, you may ask, what are the lines or edges connecting them? They represent the relationships or interactions between nodes. This is the core foundational concept behind graph theory, and it is radically utilized in areas as varied as computer science, biology, and social sciences.

Let’s paint a picture with some examples. In social network analysis, think of each node as a person. The edges are the ties that bind them – friendships, work relationships; you name it. It’s like mapping out your own social universe. Now, switch gears to biology. Here, nodes could be proteins, and the edges show us how they interact, kind of like a molecular dance...

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