9 citations · 12 across the 3 of their papers we have counts for
3 papers
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…
Convolutional neural networks for valid and efficient causal inference
Mohammad Ghasempour, Niloofar Moosavi, Xavier de Luna
Convolutional neural networks (CNN) have been successful in machine learning applications. Their success relies on their ability to consider space invariant local features. We cons…
The costs and benefits of uniformly valid causal inference with high-dimensional nuisance parameters
Niloofar Moosavi, Jenny Häggström, Xavier de Luna
Important advances have recently been achieved in developing procedures yielding uniformly valid inference for a low dimensional causal parameter when high-dimensional nuisance mod…