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
  • Target trial emulation (see working group GeTTCausal)
  • 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 Publications

    Articles with peer review

  • Braitmaier M, Didelez V. Emulierung von Target Trials mit Real World Daten - Ein allgemeines Prinzip, um den Herausforderungen von Beobachtungsdaten zu begegnen. Prävention und Gesundheitsförderung. 2023; (Epub 2022 Jul 29).
  • Morris TT, Heron J, Sanderson E, Smith GD, Didelez V, Tilling K. Interpretation of Mendelian randomization using a single measure of an exposure that varies over time. International Journal of Epidemiology. 2022;51(6):1899-1909.
  • Braitmaier M, Schwarz S, Kollhorst B, Senore C, Didelez V, Haug U. Screening colonoscopy similarly prevented distal and proximal colorectal cancer: A prospective study among 55-69-year-olds. Journal of Clinical Epidemiology. 2022;149:118-126.
  • Witte J, Foraita R, Didelez V. Multiple imputation and test-wise deletion for causal discovery with incomplete cohort data. Statistics in Medicine. 2022;41(23):4716-4743.
  • Didelez V, Stensrud MJ. On the logic of collapsibility for causal effect measures. Biometrical Journal. 2022;64(2):235-242. (This paper has been recognized as a top cited and top downloaded research article from 2021-2022 in Biometrical Journal).
  • 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. 2022;117(537):175-183.
  • Börnhorst C, Reinders T, Rathmann W, Bongaerts B, Haug U, Didelez V, Kollhorst B. Avoiding time-related biases: A feasibility study on antidiabetic drugs and pancreatic cancer applying the parametric g-formula to a large German healthcare database. Clinical Epidemiology. 2021;(13):1027-1038.
  • Andrews R, Didelez V. Insights into the cross-world independence assumption of causal mediation analysis. Epidemiology. 2021;32(2):209-219.
  • 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.
  • Stensrud MJ, Hernán MA, Tchetgen Tchetgen E, Robins JM, Didelez V, Young JG. A generalized theory of separable effects in competing event settings. Lifetime Data Analysis. 2021;27(4):588-631. (This paper was one of 2021’s top downloaded Lifetime Data Analysis research articles).
  • 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.
  • 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. (This paper has been recognized as a top cited research article from 2020-2021 in Biometrical Journal).
  • Witte J, Henckel L, Maathuis MH, Didelez V. On efficient adjustment in causal graphs. Journal of Machine Learning Research. 2020;21(246):1-45.
  • 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.
  • 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 and one of 2021's top cited Lifetime Data Analysis research articles).
  • Witte J, Didelez V. Covariate selection strategies for causal inference: Classification and comparison. Biometrical Journal. 2019;61(5):1270-1289.
  • 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.
  • Farewell D, Huang C, Didelez V. Ignorability for general longitudinal data. Biometrika. 2017;104(2):317-326.
  • Book chapters

  • 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.
  • 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.
  • Presentations at scientific meetings/conferences (invited)

  • Didelez V. Causal estimands and interventions. "Learning from Interventions"-Workshop at the Simons Institute for the Theory of Computing, 14-17 February 2022, Berkeley, USA.
  • Didelez V. Causal reasoning in survival and time-to-event analyses. Online Causal Inference Seminar, 1 December 2020, online presentation.
  • Posters at scientific meetings/conferences

  • Bang CW, Witte J, Foraita R, Didelez V. Efficient use of temporal background knowledge for causal discovery with cohort data. American Causal Inference Conference (ACIC), 23-25 May 2022, Berkeley, USA.
  • Foraita R, Witte J, Börnhorst C, Pigeot I, Didelez V, on behalf of the I.Family and GrowH! consortia. A longitudinal causal graph analysis investigating modifiable risk factors and obesity in a European cohort of children and adolescents. 6. Konferenz der Deutschen Arbeitsgemeinschaft Statistik (DAGStat), 28. März-1. April 2022, Hamburg.
  • Commentaries

  • Didelez V. Seconder of the vote of thanks to Vansteelandt and Dukes and contribution to the Discussion "Assumption-lean inference for generalized linear model parameters". Journal of the Royal Statistical Society. Series B (Statistical Methodology). 2022;84(3):689-691.
  • Fulcher I, Shpitser I, Didelez V, Zhou K, Scharfstein DO. Discussion on "Causal mediation of semicompeting risks" by Yen-Tsung Huang. Biometrics. 2021;77(4):1165-1169.