Workshop: Propensity score methods for estimating causal effects in non-experimental studies: The why, what, and how


Monday, 21 October, 9 am - 5 pm


UTS, Sydney

Propensity scores are an increasingly common tool for estimating the effects of interventions in observational (“non-experimental”) settings and for answering complex questions in randomized controlled trials. They can be of great use in epidemiologic research, for example helping assess broad population effects of exposures, programs, or policies. This workshop will discuss the importance of the careful design of observational studies, and the role of propensity scores in that design, with the main goal of providing practical guidance on the use of propensity scores to estimate causal effects. The workshop will cover the primary ways of using propensity scores to adjust for confounders when estimating the effect of a particular “cause” or “intervention”, including weighting, subclassification, and matching. Topics covered will include how to specify and estimate the propensity score model, selecting covariates to include in the model, diagnostics, and common challenges and solutions. Software for implementing analyses using propensity scores will also be discussed. The workshop will also highlight recent advances in the propensity score literature, including prognostic scores, covariate balancing propensity scores, methods for non-binary treatments, and approaches to be used when there are large numbers of covariates available (as in claims data).

This workshop is presented by Prof Elizabeth A. Stuart from the Department of Mental Health at the Johns Hopkins Bloomberg School of Public Health, with joint appointments in the Department of Biostatistics and the Department of Health Policy and Management, and Associate Dean for Education at JHSPH. Professor Stuart received her PhD in statistics in 2004 from Harvard University and is a Fellow of the American Statistical Association. She has extensive experience in methods for estimating causal effects and dealing with the complications of missing data in experimental and non-experimental studies, particularly as applied to mental health, public policy, and education.

The workshop is suitable for a varied audience, ranging from people with no experience with propensity scores to those with some experience who want to learn more, especially about various data complexities. General knowledge of regression and logistic regression is useful.

Registrations for this event close strictly on 14 October 2019, will the Early Bird registration deadline being set for 30 September.