1 citations · 1 across the 3 of their papers we have counts for
3 papers
stat.ML2026
Tuning Derivatives for Causal Fairness in Machine Learning
Filip Edström, Guilherme W. F. Barros, Tetiana Gorbach +1
Artificial-intelligence systems are becoming ubiquitous in society, yet their predictions typically inherit biases with respect to protected attributes such as race, gender, or age…
stat.ME2024★ 1 cited
Impact of Non-Informative Censoring on Propensity Score Based Estimation of Marginal Hazard Ratios
Guilherme W. F. Barros, Jenny Häggström
In medical and epidemiological studies, one of the most common settings is studying the effect of a treatment on a time-to-event outcome, where the time-to-event might be censored…
stat.ME2024
Covariate selection for the estimation of marginal hazard ratios in high-dimensional data
Guilherme W. F. Barros, Jenny Häggström
Hazard ratios are frequently reported in time-to-event and epidemiological studies to assess treatment effects. In observational studies, the combination of propensity score weight…