Abstract: While much of science is concerned with the effects of causes, relying upon evidence accumulated from randomized controlled experiments and observational studies, the problem of inferring the causes of effects arises in many practical policy and legal contexts. Discussions of the two concepts, "the effects of causes" and "the causes of effects," go far back in the philosophical literature but remain murky. The statistical literature is only of limited help here as well, focusing largely on the tradition problem of the "effects of causes." Through a series of examples, I review the two concepts, how they are related, and how they differ. I discuss the challenges for statisticians who should be worried about both problems.
Bio: Stephen E. Fienberg is Maurice Falk University Professor of Statistics and Social Science at Carnegie Mellon University, and co-director of the Living Analytics Research Centre (jointly operated by Carnegie Mellon and Singapore Management University), with appointments in the Department of Statistics, the Machine Learning Department, the Heinz College, and Cylab. He joined the faculty of Carnegie Mellon in 1980. He received his Ph.D. in Statistics from Harvard University in 1968 and has served on the faculties of the University of Chicago, University of Minnesota, and York University. Fienberg’s research includes the development of statistical methods, especially tools for the analysis of categorical data, networks, and privacy protection, from both likelihood and Bayesian perspectives. His current research also includes aspects of the history of statistics, statistics and the law, methodology for census-taking, and the use of algebraic and polyhedral geometry in statistical methodology and theory. He is a member of the U. S. National Academy of Sciences (elected 1999), and a fellow of the Royal Society of Canada, the American Academy of Arts and Sciences, and the American Academy of Political and Social Science.