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.
Biometrika , volume=
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Derives new analytical sample size and power formulas for marginal hazard ratios in causal inference with time-to-event outcomes, applicable to randomized trials and observational studies via IPW estimators.
Observational and counterfactual distributions are linked by identical support and invariant features, enabling a flow-matching estimator with semiparametric efficiency correction to generate debiased counterfactuals from observations.
A calibration procedure yields a weighted transported average treatment effect with asymptotically valid and efficient inference when experimental data grows slower than observational data, even without positivity or correct OLS specification.
citing papers explorer
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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.
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Sample size and power calculations for causal inference with time-to-event outcomes
Derives new analytical sample size and power formulas for marginal hazard ratios in causal inference with time-to-event outcomes, applicable to randomized trials and observational studies via IPW estimators.
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Debiased Counterfactual Generation via Flow Matching from Observations
Observational and counterfactual distributions are linked by identical support and invariant features, enabling a flow-matching estimator with semiparametric efficiency correction to generate debiased counterfactuals from observations.
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Transporting treatment effects by calibrating large-scale observational outcomes
A calibration procedure yields a weighted transported average treatment effect with asymptotically valid and efficient inference when experimental data grows slower than observational data, even without positivity or correct OLS specification.