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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

References

  1. Williams DN, Williams KA. Sample Size Considerations: Basics for Preparing Clinical or Basic Research. Ann Nucl Cardiol. 2020;6(1):81-85. doi: 10.17996/anc.20-00122. Epub 2020 Aug 31. PMID: 37123495; PMCID: PMC10133938.
  2. Xie Y. Causal Inference and Heterogeneity Bias in Social Science. Inf Knowl Syst Manage. 2011 Jan 1;10(1):279-289. doi: 10.3233/IKS-2012-0197. PMID: 23970824; PMCID: PMC3747843.
  3. Hecht, C. A., Dweck, C. S., Murphy, M. C., Kroeper, K. M., & Yeager, D. S. (2023). Efficiently exploring the causal role of contextual moderators in behavioral science. PNAS Proceedings of the National Academy of Sciences of the United States of America, 120(1), 1–11.
  4. Farrokhyar, F., Skorzewski, P., Phillips, M.R. et al. When to believe a subgroup analysis: revisiting the 11 criteria. Eye 36, 2075–2077 (2022). https://doi.org/10.1038/s41433-022-01948-0
  5. Brand, J. E., Zhou, X., & Xie, Y. (2023). Recent developments in causal inference and machine...
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