2018 Fisher Lecturer Winner
  Susan A. Murphy
   Harvard University 

The 2018 Committee of Presidents of Statistical Societies (COPSS) Fisher Lectureship Committee selected Susan A. Murphy of Harvard University to deliver the Fisher Lecture at the Joint Statistical Meetings in Vancouver. The citation for Dr. Murphy’s plaque reads:

“For scientific contributions to statistical theory and methods at the highest level and for fundamental advances in the innovative use of statistics to further behavioral and mental health research."

Dr. Murphy's talk is titled "The Future: Stratified Micro-randomized Trials with Applications in Mobile Health". The lecture can be viewed online.


Technological advancements in the field of mobile devices and wearable sensors make it possible to deliver treatments anytime and anywhere to users like you and me. Increasingly the delivery of these treatments is triggered by detections/predictions of vulnerability and receptivity. These observations are likely to have been impacted by prior treatments. Furthermore the treatments are often designed to have an impact on users over a span of time during which subsequent treatments may be provided. Here we discuss our work on the design of a mobile health smoking cessation study in which the above two challenges arose. This work involves the use of multiple online data analysis algorithms. Online algorithms are used in the detection, for example, of physiological stress. Other algorithms are used to forecast at each vulnerable time, the remaining number of vulnerable times in the day. These algorithms are then inputs into a randomization algorithm that ensures that each user is randomized to each treatment an appropriate number of times per day. We develop the stratified micro-randomized trial which involves not only the randomization algorithm but a precise statement of the meaning of the treatment effects and the primary scientific hypotheses along with primary analyses and sample size calculations. Considerations of causal inference and potential causal bias incurred by inappropriate data analyses play a large role throughout.

Biography of Susan A. Murphy

Susan Murphy is a Professor of Statistics, Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University. Prior to joining Harvard University in September 2017, she was the H.E. Robbins Distinguished University Professor of Statistics, Professor of Psychiatry, and Research Professor, Institute for Social Research all at the University of Michigan.

Dr. Murphy’s present research focuses on causal inference and sequential decision making. She is particularly interested in the domain of mobile health in which sequences of treatments are provided to a user in order to help the user achieve their longer term health goals. Her lab develops experimental designs for informing sequential decision making and develops both online and after-the-study-is-over data analysis that pay special attention to the thorny causal challenges that occur in this domain. She has been very fortunate to collaborate with a wide variety of excellent scientists including Linda Collins at Pennsylvania State University. She and her lab have also been fortunate to receive much positive feedback from journal reviewers. None-the less she has managed to accumulate a large number of rejected papers!

Dr. Murphy is a member of the National Academy of Sciences and of the National Academy of Medicine, both of the US National Academies. In 2013 she was awarded a MacArthur Fellowship for her work on experimental designs to inform sequential decision making. She is a Fellow of the College on Problems in Drug Dependence, a Member of the International Statistical Institute, a Fellow of the American Statistical Association and a Fellow of the Institute of Mathematical Statistics. She is a former co-editor of the Annals of Statistics and delivered the IMS Wald Lectures in 2015. She is currently the President of the Bernoulli Society.