Professor Stijn Vansteelandt
BSc MSc PhD
of Statistical Methodology
I am Belgian and graduated as Master in Mathematics at Ghent University in 1998. I obtained a PhD in Mathematics (Statistics) in 2002 at the same university, and subsequently did postdoctoral research in the Department of Biostatistics of the Harvard School of Public Health. I joined the London School of Hygiene and Tropical Medicine in 2017, and also hold a position as Professor of Statistics in the Department of Applied Mathematics, Computer Science and Statistics at Ghent University, Belgium.
I develop statistical methods for inferring the causal effect of an exposure on an outcome from experimental and observational data under minimal and well-understood assumptions. My work focuses on a variety of topics in biostatistics, epidemiology and medicine, such as the analysis of longitudinal and clustered data, missing data, adjustment for baseline covariates in randomised experiments, mediation and moderation/interaction, instrumental variables, family-based genetic association studies, time-varying confounding, analysis of outcome-dependent samples and phylogenetic inference.
My current research is primarily aimed at making causal inferences (e.g. mediation analyses, methods for time-varying confounding adjustment) less vulnerable to the weaknesses (imprecision, finite-sample bias and susceptibility to model misspecification) of simple inverse probability weighted estimators that dominate causal inference research. I aim to realise this either by improving inverse probability weighted estimation (see my work on bias-reduced double-robust estimation) or by popularising and extending alternative estimation methods, such as g-estimation, that avoid inverse weighting. More recently, I am also developing statistical methods for post-regularisation inference (that is, how to obtain honest confidence intervals and p-values that acknowledge the uncertainty due to variable selection) and machine learning algorithms for causal inference.
A characteristic feature of my research is the use of semi-parametric models.I am finishing my term as Co-Editor of Biometrics, the leading flagship journal of the International Biometrics Society, and have previously served as Associate Editor for the journals Biometrics, Biostatistics, Epidemiology, Epidemiologic Methods and the Journal of Causal Inference. In 2020, I will join the editorial board of the Journal of the Royal Statistical Society - Series B.