Biostatistics Section Workshop

Propensity Scores: Methods, Models, and Adjustments

David Stephens, McGill University

June 14, 9:00 am to 4:00 pm


The propensity score is a key component of many causal inference procedures. After establishing the basic causal inference framework, we will outline the key methods of construction of propensity score functions, and study their core mathematical properties. We will detail the use of the propensity score in matching, inverse weighting and regression adjustments that allow the unconfounded effect of an exposure or treatment of interest to be estimated consistently. Using the framework of semiparametric inference, we will contrast the statistical properties of estimators derived using each method. We will investigate issues of model selection for the propensity score, and demonstrate the utility of judicious choice of predictors that enter into the propensity function: this will be illustrated in standard problems and also in the case of high-dimensional predictors. Longitudinal data will also be studied in the causal setting. Finally, we will develop the Bayesian framework for handling causal inference, and investigate how propensity function construction and usage translates to the new setting. Computation will be performed in R.


David Stephens is James McGill Professor in the Department of Mathematics and Statistics at McGill University, Montreal. His principal research area is Bayesian inference and computation, with interest in applications in biostatistics, genetics and time series. He is currently Editor-in-Chief of The Canadian Journal of Statistics.