Earlier this year, Wiley was proud to publish Causal Inference in Statistics: A Primer by Professors Judea Pearl and Madelyn Glymour of UCLA and Professor Nicholas P. Jewell of Berkeley.
Many of the concepts and terminology surrounding modern causal inference can be quite intimidating to the novice. Pearl, Glymour and Jewell 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.
Since publication in February, the book has been warmly received by the statistical community and rapidly earned its place in Wiley’s bestsellers on statistics.
During JSM 2016 in Chicago, Alison Oliver had the opportunity to chat to Professor Pearl about the book before a signing with Professor Jewell.
Video interview with Judea Pearl
Further questions
1. Please could you tell us more about the material for the companion website that you have written?
Yes, we have a solutions manual provided within the companion website. Three students solved all the questions and we went over them. They are available for any instructor who wants to teach. It contains a useful set of exercises on down-to-earth applications. Every student should do them in order to understand. We created a story-like feel to the solutions. It doesn’t start with a given function, it starts with a story.
2. If there is one piece of information or advice that you would wish your readers to take away from this book, what would that be?
We are in the midst of a revolution. The attitude towards causality is rapidly changing in research and it will also do so in education and we want students to have the feeling that they are part of the revolution and that they are empowered with tools to allow them to solve important problems on their own.
3. Who should read the book and why?
Theoretically every student should read it – practically only the curious one will and those are the readers we are after. As a matter of fact, every statistics instructor should read this book and pick up the gems from it to teach where he or she feels comfortable teaching. We know that ordinary instructors will hesitate and some will take time to realise that they can teach this but we expect the curious one to make a mathematical discovery answer real questions that are everyday life kind of questions in causal reasoning. During the first year, we will reach the best brain in the industry?
4. Is that why causality is of such fascination to you, that it can solve these everyday kind of problems?
Causality fascinates me because problems looked so horrible beforehand and undoable but once you get the idea and the tools and the attitude, you can do them.