Parametric models for principal causal effects produce only partial identification without principal ignorability, with association parameters for strata identifiable solely under violation of that assumption plus strong parametric constraints.
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Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.
A model-agnostic conformal selection method reformulates CATE-based beneficiary identification as multiple testing with RCT-calibrated p-values and FDR control, allowing external data for model training.
ShrinkageTrees is an R package implementing regularized Bayesian tree ensembles for survival outcomes and causal inference via AFT models, including the first Horseshoe Forest implementation.
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Partial identification of principal causal effects under violations of principal ignorability
Parametric models for principal causal effects produce only partial identification without principal ignorability, with association parameters for strata identifiable solely under violation of that assumption plus strong parametric constraints.
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To select or not to select: predictively consistent priors instead of model selection
Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.
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A Conformal Selection Framework for Individual Treatment Beneficiaries with Auxiliary External Data
A model-agnostic conformal selection method reformulates CATE-based beneficiary identification as multiple testing with RCT-calibrated p-values and FDR control, allowing external data for model training.
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ShrinkageTrees: An R Package for Bayesian Tree Ensembles for Survival Analysis and Causal Inference
ShrinkageTrees is an R package implementing regularized Bayesian tree ensembles for survival outcomes and causal inference via AFT models, including the first Horseshoe Forest implementation.