Bhramar Mukherjee from University of Michigan writes: Congratulations to Tyler J. VanderWeele of the Harvard T.H. Chan School of Public Health, the recipient of the 2017 COPSS Presidents’ Award. This award is presented annually to a young member of one of the participating societies of the Committee of Presidents of Statistical Societies (COPSS) in recognition of outstanding contributions to the profession of statistics. The award citation recognized Professor VanderWeele: “for fundamental contributions to causal inference and the understanding of causal mechanisms; for profound advancement of epidemiologic theory and methods and the application of statistics throughout medical and social sciences; and for excellent service to the profession including exceptional contributions to teaching, mentoring, and bridging many academic disciplines with statistics.”
Tyler VanderWeele was born in Chicago, Illinois, and raised in San Jose, Costa Rica, Bulgaria and Austria. He received his BA in Mathematics from the University of Oxford in 2000 and also completed there the requirements for a second BA in Philosophy and Theology. He received an MA in Finance from the Wharton School, University of Pennsylvania in 2002, and his PhD in Biostatistics at Harvard University in 2006, with his dissertation entitled Contributions to the Theory of Causal Directed Acyclic Graphs, with James Robins as dissertation advisor. He began as an Assistant Professor of Biostatistics at the University of Chicago, Department of Health Studies (now Public Health Sciences) in 2006, returning to Harvard as Associate Professor of Epidemiology in the Departments of Epidemiology and Biostatistics in 2009. He was promoted to Full Professor with tenure at Harvard University in 2013, and was just appointed the John L. Loeb and Frances Lehman Loeb Professor of Epidemiology.
His research concerns methodology for distinguishing between association and causation in observational studies, and the use of statistical and counterfactual ideas to formalize and advance epidemiologic theory and methods. Within causal inference, he has made important contributions to theory and methods for mediation, interaction, and spillover effects; theory for causal directed acyclic graphs; methodologies for sensitivity analysis for unmeasured confounding; and philosophical foundations for causal inference. He has also made contributions to measurement error and misclassification, to the formalization of epidemiologic concepts, and to study design. His empirical research spans psychiatric, perinatal, and social epidemiology; the science of happiness and flourishing; and the study of religion and health, including both religion and population health and the role of religion and spirituality in end-of-life care. In the twelve years following the receipt of his PhD, he has published over 250 papers in peer-reviewed journals, including 140 first- or sole- author papers in premier statistics, biomedical, and social science journals; he is author of the book Explanation in Causal Inference: Methods for Mediation and Interaction (Oxford University Press). He has served on the editorial boards of Annals of Statistics, Journal of the Royal Statistical Society Series B, Epidemiology, American Journal of Epidemiology, and Sociological Methods and Research. He is co-founder and editor-in-chief of the journal Epidemiologic Methods. He also serves Co-Director of the Initiative on Health, Religion and Spirituality, faculty affiliate of the Harvard Institute for Quantitative Social Science, and Director of the Program on Integrative Knowledge and Human Flourishing at Harvard University. In addition to being the recipient of the 2017 COPSS Presidents’ Award from the Committee of Presidents of Statistical Societies, he was the recipient of the 2013 Bradford Hill Memorial Lecture, the 2014 Mortimer Spiegelman Award, the 2015 Causality in Statistics Education Award, and the 2017 John Snow Award. He lives in Cambridge, Massachusetts, with his wife Elisabeth and their son Jonathan.
Read an interview with Tyler below.
You can watch last year’s COPSS Awards presentation ceremony, and the 2017 R.A. Fisher Lecture, at JSM Baltimore, at
Tyler VanderWeele: Q&A
Tyler VanderWeele, 2017 COPSS Presidents’ Award recipient, graciously agreed to respond to Bhramar Mukherjee’s questions:
What was your reaction to winning the prestigious COPSS Presidents’ award?
I was delighted, and in a state of shock! My wife jumped for joy. A happy, almost mindless, daze set in. It was a Sunday afternoon and we went on a beautiful walk with our son through Cambridge and Harvard Yard. It was a very happy afternoon and evening. As it turned out, however, I had also contracted norovirus the night before, so I will perhaps never how much of the mindless daze was from COPSS or from… well, we won’t go into the aftermath!
Which part of your job do you like the most?
It would be a toss-up between having long stretches of time to think and to write (now sadly less frequent) and having such wonderful colleagues and students to work with. On the one hand, little makes me happier or more at peace than having an empty day to read, think, scribble out mathematics, or write. On the other hand, much of the deepest joy comes from the sharing of ideas, and developing them with colleagues and students. Unfortunately, the two increasingly seem to come into conflict due to limited time! I often wish there were 36 hours in a day.
What advice would you give to young people who are entering the profession as PhD students and assistant professors at this time?
My doctoral dissertation advisor, Jamie Robins, has consistently said to just pursue what you love and are interested in. I think that was very good advice and I would offer the same. In soft money environments especially (which is what many biostatisticians at least have to deal with), it is all too easy for one’s time and effort and creativity to be devoted to what is funded rather than what is important. I think it is essential not to confuse the means with the ends. The grants are meant to support research and the pursuit of knowledge; the pursuit of knowledge is not done for the sake of the grant! I think it is important to always be working on research questions that are significant and of interest and not just what happens to be around. I think it is also important to block out time to read broadly, to think deeply, to ponder the structure of our discipline and its relation to others. These things are essential in the choice of research questions. I have come to believe more and more strongly over my career that a substantial amount of time should be devoted to thinking about what is worthwhile pursuing and why. My hope is that universities and departments would do whatever they can to provide protected time for junior faculty (and all faculty!) to engage in deep reflective thought on important questions, whether those topics are funded or not.
Who are your most significant mentors? How did/do they impact your career?
I have had a number of wonderful mentors throughout life, I am very grateful to them. Charles Batty, my Analysis tutor in Mathematics at St. John’s College, Oxford, was an important mentor in encouraging careful, rigorous thought and probing the boundaries of concepts. Also at Oxford, my philosophy tutor Peter Hacker, an expert on Wittgenstein, taught me about the philosophy of language and about the drawing of distinctions between concepts, paying careful attention to how language is used. Believe it or not, that mentoring has been of tremendous value in trying to mathematically formalize and make more rigorous various epidemiologic concepts. At Harvard, Jamie Robins was a wonderful guide as I carried out original methodological research projects and he has constantly challenged me to think clearly and deeply about ideas and concepts, to focus on what seems most central and important. I have had many other important mentors throughout the years but in terms of my work in statistics, biostatistics and epidemiology, these would be the most important.
Why were you drawn to causal inference?
Before I began studies in biostatistics, I was actually in a doctoral program in finance. We would fit regression models and then would seem to interpret all of the regression coefficients the same way, often with some vague notion that the interpretation might be causal. It made me very uncomfortable. I felt that we were not really justified in interpreting the regression coefficient as we did, but I also felt that I lacked the technical vocabulary to express my concerns. After a while, I decided to leave finance and took a course in epidemiology and came across the concept of “confounding” and realized immediately that this was the concept that I had wanted to employ in my critique of what we had been doing in empirical finance. The next semester I began doctoral studies in biostatistics at Harvard and my very first semester there I took a course with Donald Rubin on Causal Inference and was introduced to the potential outcomes notation, and immediately saw the concept of confounding could be mathematically formalized by using such potential outcomes notation. I knew at that point that I wanted to pursue causal inference. The next year I took another, more advanced, course on causal inference with Jamie Robins at the School of Public Health at Harvard, and was introduced to causal inference with time-varying exposures, causal diagrams, and questions of mediation, which have subsequently become some of the topics of my own methodological research, much of which is summarized in my book Explanation in Causal Inference: Methods for Mediation and Interaction. I think having a formal framework to distinguish between association and causation is central. It is extremely important in the biomedical and social sciences. It is helpful, but perhaps not absolutely essential, when we are talking about the effects of a single exposure since, in that case, many of our intuitions and traditions that have been built up over the years work reasonably well. However, once we come to more nuanced inquiries concerning exposures that vary over time, or questions of mediation and mechanisms, or how we think about the causal effects on some secondary outcome in the presence of death that may precede our outcome measurement, it becomes extremely difficult to make progress in thinking about causality without a more formal framework. Counterfactuals and the potential outcomes model provide the necessary framework. The framework’s capacity to clarify and evaluate assumptions and to provide much more precise and nuanced interpretation to our estimands is extraordinary. A lot of work, however, still needs to be done in making these approaches standard practice in empirical research. For example, methods for sensitivity analysis for unmeasured confounding have been around for decades but are still rarely used in practice. In thinking about how to encourage broader use, a few months ago, in a paper in the Annals of Internal Medicine, I introduced a new metric called the E-value to assess the robustness of associations to potential unmeasured confounding (essentially related to the evidence for causality) that I hope will help standardize and promote the use of sensitivity analysis throughout the biomedical and social sciences. The formal work in causal inference using counterfactuals has constituted a massive advance in our capacity to reason about causality, and in understanding our limits in being able to do so. It has been a joy to be able to contribute to this important field.
Anything else you’d like to share about our profession?
I think that statistics as a discipline is under-appreciated in the university. It really provides the methodological foundation for so many other disciplines. It is interesting to go down the list of Departments in a university and think about how many of them use regression models, for example. Statistics has become one of academia’s major epistemologies, one of the ways we come to knowledge. I think it needs to be better acknowledged as such throughout the university. At the same time, I think that the use of statistics is often not adequately scrutinized. In many disciplines, even in statistics itself, we will often blindly accept the interpretation of some analysis without thinking critically about the interpretation, the degrees of evidence, and the assumptions that underlie the conclusions. The field of causal inference is of course helpful in this regard. But I think that the concerns are even broader. How do our statistical analyses relate to the pursuit of knowledge? When are we willing as a community to say that we know something on the grounds of statistical analyses? When is it the case that the evidence is such that it seems impossible that it will be overturned? The much discussed of late “replication crisis” has I think helped bring these important issues up quite dramatically. I also think it is possible that we sometimes overuse and over-rely upon statistics. I am sometimes surprised how in some papers, a policy conclusion is thought to immediately follow from a particular statistical analysis, when a number of ethical and value-related questions must also go into decision-making. Because statistical analyses are quantitative they seem more objective, and we have perhaps become too weak at other forms of ethical and practical reasoning, so that we, at times, perhaps over-rely on statistics in our thinking. In my view, statistics, as a discipline, is thus paradoxically under-appreciated, over-utilized, and under-scrutinized. I think additional reflection, and also education in the broader academic community on how statistical analyses are ultimately related to knowledge, would help increase the appreciation of our discipline and at the same time lead to better and more appropriate interpretation. I hope to spend a fair bit of time thinking further about this task in the years ahead, and hope that other statisticians will do the same.
Finally, what are your hobbies/interests beyond statistics?
I very much enjoy classical music and playing the piano, and I try to attend concerts whenever possible, though with a two-year-old now that has become a little less frequent. More and more time has been devoted to my family life, which I have thoroughly enjoyed. I enjoy food and wine… perhaps too much! And I also very much enjoy tennis and, in times past (and hopefully future), skiing. I’ve been fairly involved in various church communities throughout my life and this has been an important part of the way I think about and understand the world, and more recently this has also been part of my academic work with empirical studies on religion and health. I still enjoy opportunities to read more in philosophy and theology and some of my more recent work has also been thinking about how ideas in philosophy and theology might inform empirical statistical research in the social and biomedical sciences and vice versa… but now I am talking about work once again. Probably more balance with other interests and hobbies, family and friends would be good!
Congratulations, Tyler VanderWeele, on behalf of the community. We wish you continued success with your amazingly creative scholastic career in the years to come!