Cong Jiang is a postdoctoral researcher in the Faculté de Pharmacie at the Université de Montréal. He obtained his PhD in statistics from the University of Waterloo in 2022, under the supervision of Drs. Mary Thompson and Michael Wallace. His research specialization lies in developing methods for dynamic treatment regimes with interference, particularly focusing on applications to household smoking cessation. He is also directing his research towards machine learning and semiparametric/nonparametric efficiency in causal inference, with a particular emphasis on vaccine effectiveness estimation within infectious diseases.

Michael Wallace is an associate professor in the Department of Statistics and Actuarial Science at the University of Waterloo. Michael's research is centred around causal inference, with a focus on precision medicine. Within the precision medicine framework, Michael studies complex data, including measurement error, interference in networks, and competing outcomes. He is also engaged with numerous collaborative projects, especially with colleagues in the field of nutrition and dietary data. Outside of research, Michael is committed to statistical communication, having served on the editorial board of Significance magazine for over a decade, and has made dozens of appearances in print, radio, and television interviews.

Mary Thompson is distinguished professor emerita in statistics and actuarial science at the University of Waterloo, where she has taught since obtaining her PhD in probability theory in 1969. Her research interests are in survey methodology, analysis of social network data, biostatistics and estimation theory.  Mary currently works on the design, preparation and analysis of complex longitudinal survey data with the International Tobacco Control Project and the Canadian Longitudinal Study on Aging.  

The article entitled "Dynamic treatment regimes with interference" by Cong Jiang, Michael P. Wallace, and Mary E. Thompson is recognized for creativity, excellence, and presentation. The article explores dynamic treatment regimes, sequences of decision rules that take individual patient information as input data and then output treatment recommendations. The dynamic treatment regime estimation method of dynamic weighted ordinary least squares is investigated, which boasts of easy implementation and the so-called double-robustness property but relies on the assumption of no interference.