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

(ebook) (audiobook) (audiobook) Książka w języku angielskim
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

Causal Inference in R. Decipher complex relationships with advanced R techniques for data-driven decision making Subhajit Das - audiobook MP3

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

Ocena:
Stron:
106
Determining causality in data is difficult due to confounding factors. 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 practical chapters that blend theory with hands-on examples, illustrating how to apply advanced statistical methods to real-world scenarios. The chapters help you 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 in making 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 equipped 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.

Packt Publishing - inne książki

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