2 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.ME2025
Complementary strengths of the Neyman-Rubin and graphical causal frameworks
Tetiana Gorbach, Xavier de Luna, Juha Karvanen +1
This article contributes to the discussion on the relationship between the Neyman-Rubin and the graphical frameworks for causal inference. We present specific examples of data-gene…