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The companion website [ is a valuable resource and provides answers to the many study questions throughout the book that help with learning and understanding (it is not straightforward to register with Wiley for this and you are initially taken to a site that appears to advertise the book only, but if you can negotiate the site, it will help you get the most out of the book). Chapter 4 then introduces the concept of counterfactuals, and discusses how we can compute them and what sorts of questions we can answer using them.
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Chapter 3 is concerned with how to make predictions using causal models. Chapter 2 explains how causal models are reflected in data, and how one might search for models that explain a given data set graphical methods–in particular causal directed acyclic graphs (DAGs)–are introduced. Chapter 1 introduces the fundamental concepts of causality, including the causal model.
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Given the complex nature of some of the concepts and methods covered, particularly for those who are not familiar with them, the book is remarkably accessible and clearly written. We would add that the same is true in epidemiology, and that whereas there are debates about the relative prominence of these tools (as illustrated in recent papers and correspondence in the IJE 1, 9–15), it is essential that biostatisticians and epidemiologists alike are familiar and comfortable with these tools. They note that the development of new tools for causal inference over the decade has not excited statistical educators and that they are ‘essentially absent from statistics textbooks, especially at the introductory level’. 8 Their motivation, set out in the preface, is that ‘statisticians are invariably motivated by causal questions’ but that the ‘peculiar nature of these questions is that they cannot be answered, or even articulated, in the traditional language of statistics’. 6, 7 It is therefore of considerable interest that Pearl, together with Madelyn Glymour and Nicholas Jewell, has now produced a primer Causal Inference in Statistics. However, like War and Peace or Finnegan’s Wake, although most epidemiologists have by now heard of Pearl’s work, we suspect that relatively few have read it, at least not in the form of the original texts. What we previously tried to understand using words, probabilities and numerical examples can now be explored using causal diagrams, so that mind-bending problems such as Berkson’s Bias can be explained and understood relatively easily. 1 The resulting toolkit, particularly the use of counterfactual concepts and directed acyclic graphs (DAGs) has been extended by some epidemiologists to remarkable effect, 2, 3 so that some problems which were previously almost intractable can now be solved relatively easily. Pearl’s most striking contribution has been his marriage of the counterfactual and probabilistic approaches to causation. It is perhaps not too great an exaggeration to say that Judea Pearl’s work has had a profound effect on the theory and practice of epidemiology.