Statistical Methods for Causal Inference Research Group

This unit is concerned with statistical methods aiming at drawing reliable conclusions from observational data so as to assess the consequences of specific (sometimes hypothetical) interventions. Such insights are crucial for planning future intervention studies, and ultimately for public health decision making and policies, especially in the context of preventative measures and strategies. The particular statistical challenge consists in adequately addressing weaknesses and limitations of the data, such as lack of, or imperfect, randomization, systematic selection or drop-out etc. These need to be accounted for by suitable statistical models and methods.

The unit focusses on investigating and comparing the theoretical and practical properties of such methods, and aims at developing new or modifying existing methods especially relevant to the statistical analyses carried out at BIPS. A prerequisite is the in-depth understanding and scrutinizing of the underlying assumptions so that these can be made plausible, either empirically or based on subject matter knowledge.

The research interests of this unit are:

  • Confounder selection and adjustment
  • Models and methods for causal mediation analysis
  • Causal models and methods for time-dependent data, especially time-varying exposures
  • Instrumental variables, especially “Mendelian Randomisation”
  • Bias modelling and analysis

Selected publication

    Articles with peer-review

  • Stensrud MJ, Young JG, Didelez V, Robins JM, Hernán MA. Separable effects for causal inference in the presence of competing events. Journal of the American Statistical Association. 2021; (Epub 2020 May 15).
    https://doi.org/10.1080/01621459.2020.1765783
  • Andrews R, Didelez V. Insights into the cross-world independence assumption of causal mediation analysis. Epidemiology. 2021;32(2):209-219.
    https://doi.org/10.1097/EDE.0000000000001313
  • Brydges CR, Carlson MC, Andrews R, Rebok GW, Bielak AAM. Using cognitive intraindividual variability to measure intervention effectiveness: Results from the Baltimore experience corps trial. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences. 2021;76(4):661-670.
    https://doi.org/10.1093/geronb/gbaa009
  • Witte J, Henckel L, Maathuis MH, Didelez V. On efficient adjustment in causal graphs. Journal of Machine Learning Research. 2020;21(246):1-45.
    http://jmlr.org/papers/v21/20-175.html
  • Andrews R, Shpitser I, Lopez O, Longstreth WT, Chaves PH, Kuller L, Carlson MC. Examining the causal mediating role of brain pathology on the relationship between diabetes and cognitive impairment: The Cardiovascular Health Study. Journal of the Royal Statistical Society. Series A (Statistics in Society). 2020;183(4):1705-1726.
    https://doi.org/10.1111/rssa.12570
  • Foraita R, Friemel J, Günther K, Behrens T, Bullerdiek J, Nimzyk R, Ahrens W, Didelez V. Causal discovery of gene regulation with incomplete data. Journal of the Royal Statistical Society. Series A (Statistics in Society). 2020;183(4):1747-1775.
    https://doi.org/10.1111/rssa.12565
  • Aalen OO, Stensrud MJ, Didelez V, Daniel R, Roysland K, Strohmaier S. Time-dependent mediators in survival analysis: Modeling direct and indirect effects with the additive hazards model. Biometrical Journal. 2020;62(3):532-549.
    https://doi.org/10.1002/bimj.201800263
  • Witte J, Didelez V. Covariate selection strategies for causal inference: Classification and comparison. Biometrical Journal. 2019;61(5):1270-1289.
    https://doi.org/10.1002/bimj.201700294
  • Didelez V. Defining causal mediation with a longitudinal mediator and a survival outcome. Lifetime Data Analysis. 2019;25(4):593-610. (This paper was one of 2020’s top downloaded Lifetime Data Analysis research articles.).
    https://doi.org/10.1007/s10985-018-9449-0
  • Vansteelandt S, Didelez V. Improving the robustness and efficiency of covariate-adjusted linear instrumental variable estimators. Scandinavian Journal of Statistics, Theory and Applications. 2018;45(4):941-961.
    https://doi.org/10.1111/sjos.12329
  • Farewell D, Huang C, Didelez V. Ignorability for general longitudinal data. Biometrika. 2017;104(2):317-326.
    https://doi.org/10.1093/biomet/asx020
  • Contributions to books and proceedings

  • 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. Boca Raton: Chapman & Hall/CRC. 2018. S. 87-115.
  • Didelez V. Causal concepts and graphical models. In: Maathuis M, Drton M, Lauritzen SL, Wainwright M, editors. Handbook of graphical models. Boston: CRC Press. 2018. S. 353-380.
  • Presentations at scientific meetings/conferences (invited)

  • Didelez V. Causal reasoning in survival and time-to-event analyses. Online Causal Inference Seminar, 1 December 2020, online presentation.

Staff

Bang, Christine Winther
bang(at)leibniz-bips.de

Börnhorst, Claudia, Dr.
Tel.: +49 (0)421 218-56946
Fax: +49 (0)421 218-56941
boern(at)leibniz-bips.de

Didelez, Vanessa, Prof. Dr.
Tel.: +49 (0)421 218-56939
Fax: +49 (0)421 218-56941
didelez(at)leibniz-bips.de

Witte, Janine
Tel.: +49 (0)421 218-56938
Fax: +49 (0)421 218-56941
witte(at)leibniz-bips.de