Dr. Donald B. Rubin from Harvard University will be our 49th Hogg and Craig Lecturer.

Early in the 1969-70 academic year, Professor Allen T. Craig announced his retirement. He gave a retirement talk in January 1970. Under the leadership of Craig’s student and co-author, Professor Robert V. Hogg, the department decided to establish a lecture series to honor Professor Craig. His January 1970 talk was the first in this series. When Professor Hogg passed away at the age of 90 in 2014, the department decided to incorporate his name into the lecture series.

https://stat.uiowa.edu/hogg-and-craig-lectures

Donald B. Rubin is currently Professor in the Yau Center for Mathematical Sciences, Tsinghua University; Murray Schusterman Senior Research Fellow, Fox Business School, Temple University; and Professor of Statistics Emeritus, Harvard University. He is an elected Fellow (or Member/Honorary Member) of: the Woodrow Wilson Society, Guggenheim Memorial Foundation, Alexander von Humboldt Foundation, American Statistical Association, Institute of Mathematical Statistics, International Statistical Institute, American Association for the Advancement of Science, American Academy of Arts and Sciences, European Association of Methodology, the British Academy, and the U.S. National Academy of Sciences. As of 2022, he has authored/coauthored nearly 500 publications (including 10 books), has four joint patents, and for many years has been one of the most highly cited authors in the world, with currently approximately 350,000 citations, and over 20,000 per year in recent years (Google Scholar). Of his many publications with over 1,000 citations each, 16 of them are solely authored by Dr. Rubin. He has received honorary doctorate degrees from Otto Friedrich University, Bamberg, Germany; the University of Ljubljana, Slovenia; Universidad Santo Tomás, Bogotá, Colombia; Uppsala University, Sweden; and Northwestern University, Evanston, Illinois. He has also received honorary professorships from the University of Utrecht, The Netherlands; Shanghai Finance University, China; Nanjing University of Science & Technology, China; Xi’an University of Technology, China; and University of the Free State, Republic of South Africa. He is a widely sought international lecturer and consultant on a variety of statistical topics.

All meetings will be conducted on Central Time.

Day 1: Thursday, April 21, 2022

2:30 PM – 2:50 PM         In-Person Presentation of Annual Student Awards (14 Schaeffer Hall)

3:15 PM – 3:30 PM         Virtual Meet and Greet

3:30 PM – 4:30 PM         Virtual Lecture #1:

Essential Concepts of Causal Inference: A Remarkable History and an Intriguing Future

Causal inference refers to the process of inferring what would happen in the future if we change what we are doing, or inferring what would have happened in the past, if we had done something different in the distant past. Humans adjust our behaviors by anticipating what will happen if we act in different ways, using past experiences to inform these choices. ‘Essential’ here means in the mathematical sense of excluding the unnecessary and including only the necessary, e.g. stating that the Pythagorean theorem works for an isosceles right triangle is bad mathematics because it includes the unnecessary adjective isosceles; of course this is not as bad as omitting the adjective ‘right.’ I find much of what is written about causal inference to be mathematically inapposite in one of these senses because the descriptions either include irrelevant clutter or omit conditions required for the correctness of the assertions. The history of formal causal inference is remarkable because its correct formulation is so recent, a twentieth century phenomenon, and its future is intriguing because it is currently undeveloped when applied to investigate interventions applied to conscious humans, and moreover will utilize tools impossible without modern computing.

Day 2: Friday, April 22, 2022

3:15 PM – 3:30 PM         Virtual Meet and Greet

3:30 PM – 4:30 PM         Virtual Lecture #2:

Conditional Calibration and the Sage Statistician

Being a calibrated statistician means using procedures that in long-run practice basically follow the guidelines of Neyman’s approach to frequentist inference, which dominates current statistical thinking. Being a sage (i.e., wise) statistician when confronted with a particular data set means employing some Bayesian and Fiducial modes of thinking to moderate simple Neymanian calibration, even if not doing so formally. This article explicates this marriage of ideas using the concept of conditional calibration, which takes advantage of more recent simulation-based ideas arising in Approximate Bayesian Computation.