148 citations · 583 across the 36 of their papers we have counts for
39 papers
The Real Deal Behind the Artificial Appeal: Inferential Utility of Tabular Synthetic Data
Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey +4
Recent advances in generative models facilitate the creation of synthetic data to be made available for research in privacy-sensitive contexts. However, the analysis of synthetic d…
Orthogonal prediction of counterfactual outcomes
Stijn Vansteelandt, Paweł Morzywołek
Orthogonal meta-learners, such as DR-learner, R-learner and IF-learner, are increasingly used to estimate conditional average treatment effects. They improve convergence rates rela…
Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation
Erin E Gabriel, Michael C Sachs, Torben Martinussen +4
There are now many options for doubly robust estimation; however, there is a concerning trend in the applied literature to believe that the combination of a propensity score and an…
Optimally weighted average derivative effects
Oliver Hines, Karla Diaz-Ordaz, Stijn Vansteelandt
Weighted average derivative effects (WADEs) are nonparametric estimands with uses in economics and causal inference. Debiased WADE estimators typically require learning the conditi…
Adjusting for time-varying treatment switches in randomized clinical trials: the danger of extrapolation and how to avoid it
Hege Michiels, An Vandebosch, Stijn Vansteelandt
When choosing estimands and estimators in randomized clinical trials, caution is warranted as intercurrent events, such as - due to patients who switch treatment after disease prog…
On Weighted Orthogonal Learners for Heterogeneous Treatment Effects
Pawel Morzywolek, Johan Decruyenaere, Stijn Vansteelandt
Motivated by applications in personalized medicine and individualized policymaking, there is a growing interest in techniques for quantifying treatment effect heterogeneity in term…