Stijn Vansteelandt is Full Professor in the Department of Applied Mathematics, Computer Science and Statistics, and Professor of Statistical Methodology in the Department of Medical Statistics at the London School of Hygiene and Tropical Medicine. He is a leading expert in causal inference, which focuses on the development of statistical methods for inferring the causal effect of an exposure on an outcome from experimental and observational data under minimal and well-understood assumptions. He has authored over 150 peer-reviewed publications in international journals on a variety of topics in biostatistics. He is Co-Editor of Biometrics, the leading flagship journal of the International Biometrics Society. His recent research primarily focuses on how to obtain valid inference (valid confidence intervals and tests) when machine learning is used in a data analysis.
Keywords: Causal inference, Post-machine-learning-inference, Semi-parametric statistics, Missing data
- VanderWeele, T.J. and Vansteelandt, S. (2010). Odds ratios for mediation analysis for a dichotomous outcome. American Journal of Epidemiology, 172, 1339-1348.
- Vansteelandt, S., Bekaert, M. and Claeskens, G. (2012). On model selection and model misspecification in causal inference. Statistical Methods in Medical Research, 21, 7-30.
- Daniel, R.M., De Stavola, B.L., Cousens, S.N. and Vansteelandt, S. (2015). Causal mediation analysis with multiple mediators. Biometrics, 71, 1-14.
- Vermeulen, K. and Vansteelandt, S. (2016). Data-Adaptive Bias-Reduced Doubly Robust Estimation. International Journal of Biostatistics, 12, 253-282.
- Steen, J., Loeys, T., Moerkerke, B. and Vansteelandt, S. (2017). medflex: An R Package for Flexible Mediation Analysis Using Natural Effect Models. Journal of Statistical Software, 76, Article 11.