Causal Inference for Everyone: A Beginner's Guide to Python Tools and Techniques

by Chris S. Alston

Causal Inference for A Beginner's Guide to Python Tools & TechniquesCausal inference isn't just a fancy term; it's the science of understanding cause and effect. It empowers you to move beyond correlations and unlock the true drivers of change, whether in healthcare, marketing, social policy, or beyond.This book is written by a passionate data scientist who started just like you, eager to demystify causal inference. You'll benefit from clear explanations, real-world examples, and practical Python tools, ensuring you grasp the concepts and apply them confidently.Summary of the Imagine asking "what if?" and getting answers! This book equips you with the knowledge and tools the fundamentals of causal inference in plain English.Master Python libraries like DoWhy, CausalML, and EconML, designed for easy causal analysis.Analyze real data from various fields and uncover hidden relationships.Make a real difference with your newfound causal inference skills.What's Foundational Demystifying causality, confounding variables, and causal inference methods.Hands-on Step-by-step guides and examples using beginner-friendly libraries.Real-world Case studies from healthcare, marketing, and other fields.Bonus GlossaryAbout the This book is for you curious about the "why" behind the data you see every day.You're a student or professional seeking to add causal inference to your skillset.You're a data enthusiast ready to move beyond basic analysis and make a real impact.Learn at your own pace! This book is designed for busy individuals. Each chapter is a bite-sized learning module, and the hands-on exercises reinforce concepts effectively.Ready to unlock the secrets of cause and effect? Click "Buy Now" and start your journey today!

124 pages, Paperback

Published February 7, 2024