HAPS constructs shorter conformal prediction sets for censored time-to-event outcomes by using time-varying covariate histories and IPCW, achieving approximate coverage among survivors with up to 75% shorter intervals in simulations.
Biometrika , volume=
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Proposes influence function projection exploiting graphical independence constraints for more efficient semiparametric estimation of bounds on average causal effects under sensitivity models for unmeasured confounding.
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History-Aware Conformal Prediction Sets for Censored Time-to-Event Outcomes
HAPS constructs shorter conformal prediction sets for censored time-to-event outcomes by using time-varying covariate histories and IPCW, achieving approximate coverage among survivors with up to 75% shorter intervals in simulations.
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Exploiting independence constraints for efficient estimation of bounds on causal effects in the presence of unmeasured confounding
Proposes influence function projection exploiting graphical independence constraints for more efficient semiparametric estimation of bounds on average causal effects under sensitivity models for unmeasured confounding.