New orthogonal risk functions are derived for conditional OR and RR, with simulations and NHANES data showing nonparametric estimators reduce bias compared to parametric alternatives in complex settings.
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Derives non-overlap bounds for the ATE on bounded outcomes with width proportional to non-overlap size, plus a TMLE estimator and multiplier bootstrap for inference.
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Orthogonal machine learning for conditional odds and risk ratios
New orthogonal risk functions are derived for conditional OR and RR, with simulations and NHANES data showing nonparametric estimators reduce bias compared to parametric alternatives in complex settings.
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Non-overlap Average Treatment Effect Bounds
Derives non-overlap bounds for the ATE on bounded outcomes with width proportional to non-overlap size, plus a TMLE estimator and multiplier bootstrap for inference.