Introduces the Adaptive Survival Estimator (ASE) that derives a closed-form efficiency-optimal allocation policy for estimating the average survival effect curve under right censoring.
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Introduces partial identification bounds and a double-robust SurvB-learner meta-learner for estimating robust CATE in survival analysis under informative censoring.
Unified targeted regularization framework for causal effect estimation with EDF outcomes using neural networks that jointly estimate outcome model, propensity scores, and fluctuation parameter.
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Adaptive Experimentation for Censored Survival Outcomes
Introduces the Adaptive Survival Estimator (ASE) that derives a closed-form efficiency-optimal allocation policy for estimating the average survival effect curve under right censoring.