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Research Frontiers in Causal Inference Under Hidden Confounding

Development of methods to identify and estimate causal effects when unmeasured confounders may bias traditional causal inference approaches.

Sensitivity Analysis Beyond Unmeasured Confounding
Causal Discovery in High-Dimensional Hidden Confounder Spaces
Graphical Methods for Latent Confounding Structures
Instrumental Variables in Partially Observed Systems
Bounds and Identifiability Under Structural Confounding
Machine Learning for Confounding Strength Estimation
Causal Inference from Network Data with Hidden Variables
Robust Treatment Effect Estimation Under Confounder Uncertainty
Latent Factor Models in Observational Causal Analysis
Mediation and Confounding in Incomplete Observation Regimes

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