Causal Inference for Beginners: A Developer's Guide to Causal Inference with DoWhy, CausalML, and EconML

Causal Inference for Beginners: A Developer's Guide to Causal Inference with DoWhy, CausalML, and EconML

A Developer's Guide to Causal Inference with DoWhy, CausalML, and EconML Unleash the power of causal inference with Python! This comprehensive guide equips you with the knowledge and skills to uncover hidden relationships and make data-driven decisions. Written by an experienced data scientist and researcher, this book offers a clear and practical introduction to causal inference, backed by real-world examples and case studies. Summary of the Book Dive into the world of causal inference and learn how to use Python libraries like DoWhy, CausalML, and EconML to estimate causal effects, control for confounding factors, and draw meaningful conclusions from your data. What's Inside Foundations of Causal Inference: Explore the fundamental concepts and principles of causal inference. - Python Libraries: Master the use of DoWhy, CausalML, and EconML for efficient causal analysis. - Real-World Examples: Discover practical applications of causal inference in various domains, such as marketing,healthcare, and economics. - Best Practices: Learn essential tips and tricks for conducting reliable causal inference studies. About the Reader This book is ideal for data scientists, researchers, and anyone interested in understanding the causal relationships between variables. No prior experience in causal inference is required. Don't miss out on this opportunity to gain a competitive edge in the field of data science. Invest in your knowledge and unlock the power of causal inference today! Ready to embark on your journey into causal inference? Get this book now and start exploring the fascinating world of cause and effect!