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Causal Inference in R. Decipher complex relationships with advanced R techniques for data-driven decision-making

Język publikacji: 1
Autor:
Subhajit Das
Causal Inference in R. Decipher complex relationships with advanced R techniques for data-driven decision-making Subhajit Das - okladka książki

Causal Inference in R. Decipher complex relationships with advanced R techniques for data-driven decision-making Subhajit Das - okladka książki

Serie wydawnicze:
Hands-on
Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
382
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.

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O autorze książki

Subhajit Das is an Applied Scientist at Amazon Inc., specializing in Causal Inference with a focus on natural language understanding. Notably, his current work at Amazon focuses on search ranking modeling using heterogenous causal modeling. With a PhD in Computer Science and over a decade of experience, he's a seasoned professional in AI and Machine Learning. Furthermore, his notable roles at 3M, Autodesk, Microsoft, and Bosch demonstrate his expertise in using causal inferencing as a means to deliver core customer solutions.

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