Books

I enjoy reading nonfiction books. The books below are recommended because I have found them to be very useful and/or transformative to my professional or personal life. I approach data science from a decision-making (actionable knowledge) and epidemiologic perspective.

Decision and data science books

Decision Quality

For general decision analysis start with this outstanding book. The book is from the Strategic Decisions Groups affiliated with Stanford University. It is written for a general audience and is relevant for business and personal applications.


Foundations of Decision Analysis

I consider this book the definitive book on decision analysis from Dr. Ron A. Howard, the Stanford Professor who pioneered the field. The book can be dense but absolutely worth it if you are serious about decision analysis.



Medical Decision Making

Great user-friendly book on decision analysis for medical decision making. This is the latest edition of a classic that includes Markov modeling of disease states.



Decision Making in Health and Medicine: Integrating Evidence and Values

Great book for learning about multiple objective decision making applied to public health and medical decisions.



Causal Inference in Statistics: A Primer

Dr. Judea Pearl is the 2011 winner of the ACM Turing Award, the highest distinction in computer science, "for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning." He has transformed the fields of data science, epidemiology, statistics, computer science, machine learning, artificial intelligence. This book on causal inference is a must read for all who aspire to stay current with this rapidly changing field. Be sure to review published errata.



Risk Assessment and Decision Analysis with Bayesian Networks

This is a brilliant practical book on Bayesian networks for decision analysis, risk assessments, and epidemiology. Bayesian networks are also called structural causal models or probabilistic graphical models. You will develop a deep appreciation of their power and centrality for causal inference and epidemiologic thinking. You will also learn about the limitations of traditional, frequentist statistical approaches and be converted into a Bayesian thinker.





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