This book describes a series of statistical methods for testing causal hypotheses using observational data – but it is not a statistics book. It describes a series of algorithms, derived from research in artificial intelligence (AI), that can discover causal relationships from observational data – but it is not a book about artificial intelligence. It describes the logical and philosophical relationships between causality and probability distributions – but it is certainly not a book about the philosophy of statistics. Rather, it is a user's guide, written for biologists, whose purpose is to allow the practising biologist to make use of these important new developments when causal questions cannot be answered with randomised experiments.

I have written the book assuming that you have no previous training in these methods. If you have taken an introductory statistics course – even if it was longer ago than you want to acknowledge – and have managed to hold on to some of the basic notions of sampling and hypothesis testing using statistics then you should be able to understand the material in this book. I recommend that you read each chapter through in its entirety even if you do not feel that you have mastered all the notions. This will at least give you a general feeling for the goals and vocabulary of each chapter. You can then go back and pay closer attention to the details.

The book is addressed to biologists, mostly because I am a practising biologist myself, but I hope that it will also be of interest to statisticians, scientists in other fields and even philosophers of science. I have not written the book as a textbook simply because the discipline to which the material in this book naturally belongs does not yet exist. Whatever the name eventually given to this new discipline, I firmly believe that it will exist, and be generally recognised as a distinct discipline, in the future. The questions that this new discipline addresses, and the elegance of its results, are too important for this not to be the case. Nonetheless, the chapters follow a logical progression that would be well suited to an upper-level undergraduate, or graduate, course. I have used the manuscript of this book for such a purpose, and every one of my students is still alive.