6 citations · 6 across the 4 of their papers we have counts for
3 papers · 1 filter
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…