Bests & Worsts Reviews from Amazon

Wow! I am a physician epidemiologist with a doctorate in epidemiology and I teach computational epidemiology (with R) at UC Berkeley. I had the opportunity to study biostatistics from the best professors at UC Berkeley School of Public Health (Steve Selvin, Nicolas Jewell, Richard Brand, and many more). The field of causal inference was just beginning to take off with biostatisticians piloting the plane (Mark van der Laan, Nicolas Jewell, etc.). I avoided a rigorous study of causal inference but eventually came around after studying Bayesian networks for decision analysis (FYI: Pearl pioneered Bayesian networks). Judea Pearl's Bayesian networks and causal graphs connects the fields of statistics, epidemiology, decision and computer sciences in a profoundly elegant way. His work empowers and expands the potential of "big data." This is the first book written for the general public on this topic. It will have a **huge impact**. Causality and causal reasoning is at the core of everything we see, do, and imagine. He provides a graphical tool (causal graphs) for encoding expert knowledge (including community wisdom and experience). Anyone --- yes, anyone --- can learn the basics. For additional rigor, there are structural causal models (functional equations). I now consider it data science "malpractice" to design studies, analyze data, or adjust for confounders without using causal graphs. As he covers extensively in the history of causality, human brains are wired to resist new paradigms. Be intellectually wise and humble and read this book -- you will not regret it!