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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…
stat.ME2024
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…