Causal Inference in Statistics

Beschreibung

Many of the concepts and terminology surrounding modern causal inference can be quite intimidating to the novice. Judea Pearl presents a book ideal for beginners in statistics, providing a comprehensive introduction to the field of causality. Examples from classical statistics are presented throughout to demonstrate the need for causality in resolving decision-making dilemmas posed by data. Causal methods are also compared to traditional statistical methods, whilst questions are provided at the end of each section to aid student learning.

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Judea Pearl

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Deduktion Inferenzstatistik Medical Statistics & Epidemiology Medizinische Statistik u. Epidemiologie Statistics Statistics for Social Sciences Statistik Statistik in den Sozialwissenschaften

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"Despite the fact that quite a few high-quality books on the topic of causal inferencehave recently been published, this book clearly fills an important gap: that of providinga simple and clear primer...Use ofcounterfactuals [in the final chapter] is elegantly linked to the structural causal models outlined in the previouschapters...[while]intriguing examples are used tointroduce and illustrate the main concepts and methods...Several thought provokingstudy questions, in the form of exercises, are given throughout the presentation,and they can be very helpful for a better understanding of the material andlooking further into the subtleties of the concepts introduced. In summary, there is nodoubt that a discussion of the basic ideas in causal inference should be included in allintroductory courses of statistics. This book could serve as a very useful companion tothe lectures." (Mathematical Reviews/MathSciNet April 2017)
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