6 citations · 6 across the 4 of their papers we have counts for
5 papers
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…
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…
Valid causal inference with unobserved confounding in high-dimensional settings
Niloofar Moosavi, Tetiana Gorbach, Xavier de Luna
Various methods have recently been proposed to estimate causal effects with confidence intervals that are uniformly valid over a set of data generating processes when high-dimensio…
Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency
Tetiana Gorbach, Xavier de Luna, Juha Karvanen +1
Semiparametric inference on average causal effects from observational data is based on assumptions yielding identification of the effects. In practice, several distinct identifying…
Inference for partial correlation when data are missing not at random
Tetiana Gorbach, Xavier de Luna
We introduce uncertainty regions to perform inference on partial correlations when data are missing not at random. These uncertainty regions are shown to have a desired asymptotic…