9 citations · 59 across the 32 of their papers we have counts for
5 papers · 1 filter
The Promises of Parallel Outcomes
Ying Zhou, Dingke Tang, Dehan Kong +1
A key challenge in causal inference from observational studies is the identification and estimation of causal effects in the presence of unmeasured confounding. In this paper, we i…
In Search of Robust Measures of Generalization
Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal +5
One of the principal scientific challenges in deep learning is explaining generalization, i.e., why the particular way the community now trains networks to achieve small training e…
Estimation of local treatment effects under the binary instrumental variable model
Linbo Wang, Yuexia Zhang, Thomas S. Richardson +1
Instrumental variables are widely used to deal with unmeasured confounding in observational studies and imperfect randomized controlled trials. In these studies, researchers often…
Ultra-high Dimensional Variable Selection for Doubly Robust Causal Inference
Dingke Tang, Dehan Kong, Wenliang Pan +1
Causal inference has been increasingly reliant on observational studies with rich covariate information. To build tractable causal procedures, such as the doubly robust estimators,…
Conditional Independence Beyond Domain Separability: Discussion of Engelke and Hitz (2020)
Yuexia Zhang, Linbo Wang
We congratulate Engelke and Hitz on a thought-provoking paper on graphical models for extremes. A key contribution of the paper is the introduction of a novel definition of conditi…