Introduces decision-aware proximal bridge learning using a weighted loss and regret bound to enhance optimal treatment selection in settings with hidden confounding.
and Ying, Andrew and Cui, Yifan and Shi, Xu and Miao, Wang
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
Introduces extended bridge functions and derives identification results for joint interventional distributions retaining proxy variables in proximal causal inference.
A neural doubly robust proxy causal learning framework using mean embeddings for treatment bridges provides consistent estimators for causal dose-response functions under unobserved confounding for continuous and structured treatments.
citing papers explorer
-
Decision-Aware Proximal Bridge Learning for Optimal Treatment Selection
Introduces decision-aware proximal bridge learning using a weighted loss and regret bound to enhance optimal treatment selection in settings with hidden confounding.
-
Identifying Interventional Joint Distributions via Extended Bridge Functions
Introduces extended bridge functions and derives identification results for joint interventional distributions retaining proxy variables in proximal causal inference.
-
Doubly Robust Proxy Causal Learning with Neural Mean Embeddings
A neural doubly robust proxy causal learning framework using mean embeddings for treatment bridges provides consistent estimators for causal dose-response functions under unobserved confounding for continuous and structured treatments.