Judea Pearl - Causal Inference in Statistics

This edition first published 2016

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To my wife, Ruth, my greatest mentor.

Judea Pearl

To my parents, who are the causes of me.

Madelyn Glymour

To Debra and Britta, who inspire me every day.

Nicholas P. Jewell

Judea Pearl is Professor of Computer Science and Statistics at the University of California, Los Angeles, where he directs the Cognitive Systems Laboratory and conducts research in artificial intelligence, causal inference and philosophy of science. He is a Co-Founder and Editor of the Journal of Causal Inference and the author of three landmark books in inference-related areas. His latest book, Causality: Models, Reasoning and Inference (Cambridge, 2000, 2009), has introduced many of the methods used in modern causal analysis. It won the Lakatosh Award from the London School of Economics and is cited by more than 13,000 scientific publications.

Pearl is a member of the National Academy of Sciences, the National Academy of Engineering, and a Founding Fellow of the Association for Artificial Intelligence. He is a recipient of numerous prizes and awards, including the Technion's Harvey Prize and the ACM Alan Turing Award for fundamental contributions to probabilistic and causal reasoning.

Madelyn Glymour is a data analyst at Carnegie Mellon University, and a science writer and editor for the Cognitive Systems Laboratory at UCLA. Her interests lie in causal discovery and in the art of making complex concepts accessible to broad audiences.

Nicholas P. Jewell is Professor of Biostatistics and Statistics at the University of California, Berkeley. He has held various academic and administrative positions at Berkeley since his arrival in 1981, most notably serving as Vice Provost from 1994 to 2000. He has also held academic appointments at the University of Edinburgh, Oxford University, the London School of Hygiene and Tropical Medicine, and at the University of Kyoto. In 2007, he was a Fellow at the Rockefeller Foundation Bellagio Study Center in Italy.

Jewell is a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science (AAAS). He is a past winner of the Snedecor Award and the Marvin Zelen Leadership Award in Statistical Science from Harvard University. He is currently the Editor of the Journal of the American Statistical Association Theory & Methods, and Chair of the Statistics Section of AAAS. His research focuses on the application of statistical methods to infectious and chronic disease epidemiology, the assessment of drug safety, time-to-event analyses, and human rights.

When attempting to make sense of data, statisticians are invariably motivated by causal questions. For example, How effective is a given treatment in preventing a disease?; Can one estimate obesity-related medical costs?; Could government actions have prevented the financial crisis of 2008?; Can hiring records prove an employer guilty of sex discrimination?

The peculiar nature of these questions is that they cannot be answered, or even articulated, in the traditional language of statistics. In fact, only recently has science acquired a mathematical language we can use to express such questions, with accompanying tools to allow us to answer them from data.

The development of these tools has spawned a revolution in the way causality is treated in statistics and in many of its satellite disciplines, especially in the social and biomedical sciences. For example, in the technical program of the 2003 Joint Statistical Meeting in San Francisco, there were only 13 papers presented with the word cause or causal in their titles; the number of such papers exceeded 100 by the Boston meeting in 2014. These numbers represent a transformative shift of focus in statistics research, accompanied by unprecedented excitement about the new problems and challenges that are opening themselves to statistical analysis. Harvard's political science professor Gary King puts this revolution in historical perspective: More has been learned about causal inference in the last few decades than the sum total of everything that had been learned about it in all prior recorded history.

Yet this excitement remains barely seen among statistics educators, and is essentially absent from statistics textbooks, especially at the introductory level. The reasons for this disparity is deeply rooted in the tradition of statistical education and in how most statisticians view the role of statistical inference.

In Ronald Fisher's influential manifesto, he pronounced that the object of statistical methods is the reduction of data (Fisher 1922). In keeping with that aim, the traditional task of making sense of data, often referred to generically as inference, became that of finding a parsimonious mathematical description of the joint distribution of a set of variables of interest, or of specific parameters of such a distribution. This general strategy for inference is extremely familiar not just to statistical researchers and data scientists, but to anyone who has taken a basic course in statistics. In fact, many excellent introductory books describe smart and effective ways to extract the maximum amount of information possible from the available data. These books take the novice reader from experimental design to parameter estimation and hypothesis testing in great detail. Yet the aim of these techniques are invariably the description of data, not of the process responsible for the data. Most statistics books do not even have the word causal or causation in the index.