Causal stability selection produces effect-modifier sets with explicit non-asymptotic false-positive bounds by combining cross-fitted CATE estimation and integrated path stability selection.
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BAMIFun provides Bayesian multiple imputation for functional data via low-rank penalized spline models, achieving accurate imputation and improved coverage in simulations and real datasets compared to single-imputation FPCA methods.
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Causal Stability Selection
Causal stability selection produces effect-modifier sets with explicit non-asymptotic false-positive bounds by combining cross-fitted CATE estimation and integrated path stability selection.
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BAMIFun: Bayesian Multiple Imputation for Functional Data
BAMIFun provides Bayesian multiple imputation for functional data via low-rank penalized spline models, achieving accurate imputation and improved coverage in simulations and real datasets compared to single-imputation FPCA methods.