Causal inference in R

• Verfasst von: Das, Subhajit [VerfasserIn]
• Titel: Causal inference in R
• Titelzusatz: decipher complex relationships with advanced R techniques for data-driven decision-making
• Verf.angabe: Subhajit Das
• Ausgabe: First edition.
• Verlagsort: Birmingham, UK
• Verlag: Packt Publishing Ltd.
• Jahr: 2024
• Umfang: 1 online resource (382 pages)
• Illustrationen: illustrations
• Fussnoten: Includes bibliographical references and index
• ISBN: 978-1-83763-902-1
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: R (Langage de programmation)
• Sach-SW: Analyse de régression
• Sach-SW: Prise de décision ; Méthodes statistiques
• K10plus-PPN: 1914058518
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4648215648
• Lokale URL Inst.: Zum Volltext

Abstract

Determining causality in data is difficult due to confounding factors. Written by an applied scientist specializing in causal inference with over a decade of experience, Causal Inference in R provides the tools and methods you need to accurately establish causal relationships, improving data-driven decision-making. This book helps you get to grips with foundational concepts, offering a clear understanding of causal models and their relevance in data analysis. You'll progress through chapters that blend theory with hands-on examples, illustrating how to apply advanced statistical methods to real-world scenarios. You'll discover techniques for establishing causality, from classic approaches to contemporary methods, such as propensity score matching and instrumental variables. Each chapter is enriched with detailed case studies and R code snippets, enabling you to implement concepts immediately. Beyond technical skills, this book also emphasizes critical thinking in data analysis to empower you to make informed, data-driven decisions. The chapters enable you to harness the power of causal inference in R to uncover deeper insights from data. By the end of this book, you'll be able to confidently establish causal relationships and make data-driven decisions with precision.