About Me

Research Interests

My current research focuses on applying causal inference methods in administrative health databases and other observational data to conduct target trial emulations which inform clinical and policy decision-making. This work has largely focused on evaluating screening strategies for colorectal cancer and prostate cancer using data from the Nordic-European Initiative on Colorectal Cancer (NordICC) trial and electronic health record data from the Veterans Health Administration, respectively.

Methodologically, my work focuses on addressing some of the limitations of instrumental variable methods, a statistical technique that can be used to estimate causal effects, even in the presence of unmeasured confounding. This includes (1) developing instrumental variable methods for time-varying treatments and outcomes (via g-estimation of structural models), and the application of these methods to Mendelian randomization (MR) studies; (2) evaluating and quantifying the impact of different study designs for Mendelian randomization; and (3) mitigating common sources of selection bias in Mendelian randomization studies.

Previous research (during my MSc in Epidemiology at Queen’s University and as a Data Analyst at the Center for Global Child Health at the Hospital for Sick Children) includes work in cancer epidemiology, genetics, and global child health and nutrition.

Teaching

I teach methods for causal inference at the Harvard T.H. Chan School of Public Health (as part of the Department of Epidemiology and CAUSALab), and Harvard Medical School (as part of their Master of Medical Sciences in Clinical Investigation (MMSCI) program).

You can find some of my teaching- and course-related materials here.

Education

Contact me

You can reach me via email.