Causal inference research
Photo by LiquidPlannerMy 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.
Dr. Philip J Clare, PhD
Biostatistician at the Prevention Research Collaboration, University of Sydney.
Publications
The overall effect of parental supply of alcohol across adolescence on alcohol-related harms in early adulthood—a prospective cohort study
Background and Aims: Recent research suggests that parental supply of alcohol is associated with more risky drinking and …
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.
