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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

Demonstrating instrumental variable analysis in R

To illustrate more instrumental variable analysis in R, focusing on the context of assessing rental unit prices tested by a real estate company, we’ll go through setting up the R environment, utilizing the ivreg package for 2SLS regression again. We'll be implementing the Generalized Method of Moments (GMM) using the gmm package, and conducting diagnostics tests. First, this example will involve creating synthetic data to simulate a realistic scenario involving multiple variables that might influence rental prices.

To begin, let’s install several R packages essential for instrumental variable analysis:

install.packages("AER") # For 2SLS regression
install.packages("gmm") # For GMM estimators
install.packages("lmtest") # For diagnostic tests
# Load the packages
library(AER)
library(gmm)
library(lmtest)

For this example, we’ll generate synthetic data representing various...

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