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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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Toc

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

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “We use the lm function for our outcome model as before but will adjust how we calculate the standard errors using the sandwich package.”

A block of code is set as follows:

library(MASS)
set.seed(123)
ad_data <- data.frame(ad_spend = runif(100, 100, 1000),
                      sign_ups = rpois(100, lambda=20))
# Poisson regression model
model <- glm(sign_ups ~ ad_spend, data=ad_data, family="poisson")
summary(model)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

x <- c(1, 2, 3)
factor_x <- structure(x, class = "factor",
                      .Label = c("low", "medium", "high"))

Bold: Indicates a new term, an important word, or words that you see on screen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “Found at the top left (accessible via File | New File | R Script), this is where you write and save longer code blocks or scripts for complex analyses.”

Tips or important notes

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