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The Data Analyst’s Guide to Cause and Effect offers an excellent, comprehensive, yet accessible introduction to causal inference. With a light-hearted approach, it opens up a new perspective for those accustomed to traditional statistical analysis, shedding light on crucial aspects of data interpretation. From selecting the right controls to estimating causal effects and even tackling advanced topics like missing data and the intricacies of multilevel modeling, this book is an invaluable guide for analysts seeking to move beyond mere correlation.

This is a clear and readable book with broad coverage of many ideas and methods in causal inference.

The Data Analyst's Guide offers a strongly application-focused introduction to causal inference and is an effective tool for getting data analysts into the world of causal inference and immediately into a workable project.