The paper proposes a general toolbox of orthogonal survival learners with custom weighting functions to estimate heterogeneous treatment effects robustly under treatment, censoring, and survival overlap violations.
Doubly robust estimation in missing data and causal inference models
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Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data
The paper proposes a general toolbox of orthogonal survival learners with custom weighting functions to estimate heterogeneous treatment effects robustly under treatment, censoring, and survival overlap violations.