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stat.ML2026
Geometric Causal Models
Eli N. Weinstein, David M. Blei
Scientists often seek to draw causal inferences from structured data that is not independently and identically distributed, such as spatial data, network data, or molecular data. W…
stat.ME2026★ 1 cited
Hierarchical Causal Models
Eli N. Weinstein, David M. Blei
Causal questions often arise in settings where data are hierarchical: subunits are nested within units. Consider students in schools, cells in patients, or cities in states. In the…
stat.ME2026
Adaptive Nonparametric Perturbations of Parametric Models with Generalized Bayes
Bohan Wu, Eli N. Weinstein, Sohrab Salehi +2
Parametric Bayesian modeling offers a powerful and flexible toolbox for machine learning. Yet the model, however detailed, may still be wrong, and this can make inferences untrustw…