1 citations · 1 across the 3 of their papers we have counts for
3 papers
Debiasing Synthetic Data Generated by Deep Generative Models
Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey +4
While synthetic data hold great promise for privacy protection, their statistical analysis poses significant challenges that necessitate innovative solutions. The use of deep gener…
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…
Handling time-dependent exposures and confounders when estimating attributable fractions -- bridging the gap between multistate and counterfactual modeling
Johan Steen, Pawel Morzywolek, Wim Van Biesen +2
The population-attributable fraction (PAF) expresses the proportion of events that can be ascribed to a certain exposure in a certain population. It can be strongly time-dependent…