Prof. Dr. Vanessa Didelez

since July 2016 Professor for statistics and causal inference, University of Bremen, and Department of Biometry and Data Management, Leibniz Institute for Prevention Research and Epidemiology - BIPS (deputy head of department)

2018-2020: Principal investigator, DFG grant "Causal Discovery for Cohort Data"

2015-2018: CoIlaborator on MRC grant “Development of a multilevel and mixture-model framework for modelling epigenetic changes over time”

2013-2023: Collaborator on MRC Integrative Epidemiology Unit, Theme 3: "Statistical and Econometric Methodology"

2013- June 2016: Reader in Statistics at the School of Mathematics, University of Bristol

2011-2013: Leverhulme Research Fellowship “Statistical models and methods for complex causal inference”

2009-2013: Senior Lecturer at the School of Mathematics, University of Bristol

2007-2010: CoI on MRC grant “Inferring epidemiological causality using Mendelian Randomisation”

2007-2009: Lecturer at the School of Mathematics, University of Bristol

2001-2007: Lecturer at the Department of Statistical Science, University College London

Dec 2000: PhD (Dr rer.nat.) at the Department of Statistics, University of Dortmund, Germany

1997-2000: Research Assistant at the Department of Statistics, University of Munich, Germany

1996: Diploma in "Statistics with Psychology" at the Department of Statistics, University of Dortmund, Germany

• Articles with peer-review

• Didelez V. Defining causal meditation with a longitudinal mediator and a survival outcome. Lifetime Data Analysis. 2018; (Epub 2018 Sep 14).
  http://dx.doi.org/10.1007/s10985-018-9449-0
• Phillippo DM, Dias S, Ades AE, Didelez V, Welton NJ. Sensitivity of treatment recommendations to bias in network meta-analysis. Journal of the Royal Statistical Society. Series A (Statistics in Society). 2018;181(3):843-867.
  http://dx.doi.org/10.1111/rssa.12341
• Vansteelandt S, Didelez V. Improving the robustness and efficiency of covariate adjusted linear instrumental variable estimators. Scandinavian Journal of Statistics, Theory and Applications. 2018; (Epub 2018 May 24).
  http://dx.doi.org/10.1111/sjos.12329
• Witte J, Didelez V. Covariate selection strategies for causal inference: Classification and comparison. Biometrical Journal. 2018; (Epub 2018 Oct 10).
  http://dx.doi.org/10.1002/bimj.201700294
• Farewell D, Huang C, Didelez V. Ignorability for general longitudinal data. Biometrika. 2017;104(2):317-326.
  http://dx.doi.org/10.1093/biomet/asx020
• Jones E, Didelez V. Thinning a triangulation of a Bayesian network or undirected graph to create a minimal triangulation. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2017;25(3):1750014.
  http://dx.doi.org/10.1142/S0218488517500143

• Book chapters

• Didelez V. Causal concepts and graphical models. In: Maathuis M, Drton M, Lauritzen SL, Wainwright M, editors. Handbook of graphical models. Handbooks of modern statistical methods. Fitzmaurice, Garrett, Boston: CRC Press; 2018. S. 353-380.
• Didelez V, Evans R. Causal inference from case-control studies. In: Borgan O, Breslow N, Chatterjee N, Gail M, Scott A, Wild C, editors. Handbook of statistical methods for case-control studies. Handbooks of modern statistical methods. Fitzmaurice, Garrett, Boca Raton, Florida: Chapman & Hall/CRC; 2018. S. 87-115.

• Commentaries

• Didelez V. Discussion on 'Causal inference by using invariant prediction: Identification and confidence intervals' by Peters, Bühlmann, Meinshausen. Journal of the Royal Statistical Society. Series B (Statistical Methodology). 2016;78(5):990-991.
  http://dx.doi.org/10.1111/rssb.12167.

For further publications please follow this link:

• Further publications