Assumption-Lean (Causal) Modelling and Estimation: A Paradigm Shift from Traditional Statistical Modelling

Assumption-lean causal modeling

Bias-reduced double-robust estimation

BOF staff support in the framework of an ERC-Grant

Causal inference in observational HIV/AIDS studies.

Center for Statistics

Data-adaptive causal inference under weak assumptions

Dealing with effect decomposition and intermediate confounding in causal mediation analysis

Development of a data-informed causal expert system for personalized treatment recommendations

Honest data-adaptive inference for treatment effects

Methods in Research on Research (MiRoR) / Estimation of causal effects from observational studies: how results obtained from different causal inference methods can be integrated in a meta-analyses approa

Offline and online evaluation of medical treatment decision strategies based on large scale electronic health registers

Optimal causal learning using electronic health records

Optimal estimation of nuisance paramters and models in causal inference

Powerful methods for detecting gene-gene interactions

Semiparametric mixed models for the analysis of genome wide expression data

Structural nested models for the effect of a time-varying exposure on dichtomous outcomes and survival outcomes

Dealing with multiple mediators in causal mediation analysis

Leveraging Big Data To Improve Healthcare Decision-Making

Modelling the impact of microbicides on HIV incidence and prevalence in Mombasa, Kenya

Multilevel causal mediation analysis of multiple longitudinal mediators with unknown causal structure