"A must-read for all epidemiologists and biostatisticians, due to its coverage of key principles of causal inference. Therefore, thisbook may be recommended to any methodologist in the field of health research, who strives to gain a comprehensive understanding of causal inference theoretically, and the statistical skillset to answer research questions using observational data." Myanca Rodrigues , Canada , ISCB News, June 2022. "The Effect is a gentle introduction to causality and researchdesign which is accessible to a wide audience. By intent, thebook does not overload the reader with formal notation ormathematics. Instead, the author, Nick Huntington-Klein,builds intuition through helpful examples and plots" Y. Samuel Wang , USA , Data Science in Science, February 2023. "The author clearly has achieved the goal of providing an accessible introduction to causality.

Any newcomer to causal inference would benefit from reading this book. Huntington-Klein's conversational delivery and avoidance of explicit mathematics in the first half of the text provides the reader with the building blocks to causally reason about a system. The second part strives to make technical tools accessible, and the code examples make these tools readily available for readers to try on their own data. This textbook will be a useful addition to the library of anyone studying causal discovery and inference." Hung-Ching Chang and Muchael T. Gorczyca , Biometrics: A Journal of the International Biometric Society, 2023. "Overall, this book, though very voluminous, is an excellent addition to the world of literature. The book contains a good number of examples and wonderfully drawn diagrams, that facilitate a clearer understanding of the concepts.

It is a wonderful exhibition of the parts and parcels of research design and causality." Nisar Ahmad Khan , India , Technometrics, April 2023. "A great textbook for an undergraduate introductory data science course or social science methodology course as well as a reference for beginning graduate students. It would also benefit researchers who are working with data but are wholly clear about where to start when investigating causal relationships." Brian W. Sloboda , University of Maryland, USA, International Statistical Review, 2023.