博客來Make smarter decisions by mastering causal reasoning and causal inference. Learn how to separate correlation from causation, evaluate impact, and apply evidence-based thinking-no complex math required.
Key Features:
- Learn how to separate causation from correlation in real decisions
- Apply causal inference methods without complex statistics
- Practice causal thinking with real cases and an interactive app
Book Description:
In a world dominated by data and correlations, making good decisions requires understanding what truly causes what. The Causal Mindset Handbook is a clear, non-technical guide to causal reasoning and causal inference, designed to help readers think more clearly about cause and effect.
Rather than focusing on complex statistics, the book introduces intuitive concepts and visual tools, such as causal graphs and counterfactual thinking, to evaluate claims, measure impact, and avoid common reasoning traps. Readers learn how causal inference differs from predictive models, and why correlation alone is not enough for sound decision-making.
Drawing on real-world case studies from business, policy, and everyday life, the book shows how causal thinking works when perfect experiments are not possible. Designed for managers, analysts, policymakers, and curious professionals, it combines hands-on exercises with access to an interactive companion app, enabling readers to practice evidence-based decision-making with confidence.
Foreword by Pr. Karim R. Lakhani, Harvard Business School, Digital Data Design Institute at Harvard, Laboratory for Innovation Science at Harvard.
What You Will Learn:
- Understand causality and why correlation can mislead decisions
- Distinguish predictive models from causal inference
- Use causal graphs to reason about cause and effect
- Evaluate impact with experiments and quasi-experiments
- Spot flawed causal claims in business and everyday life
- Apply causal thinking confidently to real decisions
Who this book is for:
This book is ideal for decision-makers, managers, analysts, marketers, policymakers, and curious professionals who want to improve how they evaluate evidence and make decisions. No prior background in statistics, economics, or programming is required. It is suited for readers who work with data, experiments, or performance metrics and want to better understand cause and effect without diving into technical or mathematical detail.
Table of Contents
- Understanding Causality and Its Importance
- Causation versus Prediction
- Why Is It So Hard To Prove Causality?
- Beyond Correlation: The Main Culprits
- The Causal Mindset Framework
- Randomized Experiment
- Quasi-Experimental Methods
- Correct Answer, Wrong Question: The Importance of Choosing the Right Metrics
- Embracing Uncertainty
