A long-time interest in my research has been the study of key inferential ideas behind attempts to draw causal conclusions from observational or experimental longitudinal data. In a typical situation, several individuals belonging to a study population are followed over time, with a sequence of covariate measurements and treatments being registered on each individual. Particular attention in this context is given to the important problem of potential confounding, and to conditions under which the size of causal effects can be estimated by statistical methods. My approach to these inferential problems is Bayesian, and it uses predictive distributions as summary measures of the considered causal effects. Nonparametric Bayesian modeling and corresponding computational methods are emphasized, often employing postulated monotonicity properties of the considered functions.
A second, and more recent, interest has been development of methods, also from a Bayesian perspective, for consideration of rank data.
Applications of these methods cover a wide range of research topics, and almost all are based on real data. Examples can be found from my list of publications.