Develops higher-order influence function estimators for implicitly defined parameters in non-separable structural models using U-processes theory.
Sensitivity anal- ysis for marginal structural models.arXiv preprint
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
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Higher-Order Debiased Estimators for General Treatment Models
Develops higher-order influence function estimators for implicitly defined parameters in non-separable structural models using U-processes theory.
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Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring
Introduces partial identification bounds and a double-robust SurvB-learner meta-learner for estimating robust CATE in survival analysis under informative censoring.