Causal inference research

Photo by LiquidPlanner

My personal research, which began with my PhD, is on the use of causal inference in longitudinal observational data.

To date, I have published a systematic review of the use of causal inference methods in addressing the issue of exposure-affected time-varying confounding. I also have a number of other papers currently under review, for which the code is available on my website. This includes a tutorial on the use of targeted maximum likelihood estimation in longitudinal data analysis.

Avatar
Dr. Philip J Clare, PhD

Biostatistician at the Prevention Research Collaboration, University of Sydney.

Publications

Causal models adjusting for time-varying confounding - A systematic review of the literature

Background Obtaining unbiased causal estimates from longitudinal observational data can be difficult due to exposure-affected …

Talks

Robust causal inference using non-randomized longitudinal data

A seminar on robust causal inference methods.

Code