Navigate the complexities of real-world data with "Causal Inference in the Wild," a guide designed for anyone who must move beyond correlation to uncover true cause and effect. In an age of abundant data but elusive truth, this book provides the essential framework and tools for data scientists, policymakers, business leaders, and analysts to make decisions that are both effective and responsible. It tackles the core challenge of modern analysis: most data is not generated in controlled experiments but emerges from the messy, uncontrolled interactions of daily life.

This book demystifies the principles of causal inference, translating sophisticated concepts from statistics and philosophy into an accessible and practical discipline. You will learn to employ key methods like matching, instrumental variables, and regression discontinuity designs, understanding not just how they work but the critical assumptions they rely on. The journey goes beyond technical tricks, fostering a mindset of disciplined reasoning, skepticism, and ethical responsibility. It emphasizes that in the "wild" of observational data, rigor is paramount to avoid the costly mistake of confusing patterns for causation.

"Causal Inference in the Wild" is your guide to transforming data into genuine insight. It equips you to ask better questions, challenge fragile assumptions, and draw conclusions that stand up to real-world complexities, ensuring your decisions are built on a foundation of clarity and evidence rather than misleading correlations.