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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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

absence of measurement error 29

A/B test analysis, in R 177

effect-size calculation 185

exploratory data analysis (EDA) 178, 179

interaction effects, visualizing 188-190

multivariate analysis 181-184

post-hoc analyses 187, 188

power analysis 186

result interpretation 184

statistical testing 179, 180

synthetic data, generating 178

t-test assumptions, checking 184, 185

A/B testing 162

blinding 169

challenges 175

common pitfalls 175

control and treatment groups, setting up 169

data collection methods, adopting 170

placebo controls 170

A/B test plan 163

hypotheses, formulating 163

randomization 167

randomization techniques 168

sample size, determining 166, 167

test statistics 164, 165

acyclic 61

adaptive experimentation...

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