The Q-Learner decomposes ratio CATE into odds ratios for propensity-based estimation and introduces doubly robust meta-learners that perform well on RCT and observational datasets.
Orthogonal statistical learning
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Develops Grenander-type and debiased machine learning estimators for the sublevel-set probability curve of the CATE function, shown to be non-pathwise differentiable, along with its piecewise linear approximation.
Develops optimal encouragement policies distinguishing responsiveness from efficacy, targeting induced take-up for fairness under budget constraints in non-adherence settings.
Develops m-th order estimators for dose-response functions based on higher-order influence functions that attain the fastest known convergence rates under stated conditions.
A large-deviations method generates plausible stress scenarios for financial losses by concentrating on most likely configurations conditional on large losses, recovering stressed loss laws even with sparse data.
citing papers explorer
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Beyond Differences: Doubly Robust Meta-Learners for Ratio-Based Treatment Effects
The Q-Learner decomposes ratio CATE into odds ratios for propensity-based estimation and introduces doubly robust meta-learners that perform well on RCT and observational datasets.
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Nonparametric inference for sublevel-set probabilities of conditional average treatment effect functions
Develops Grenander-type and debiased machine learning estimators for the sublevel-set probability curve of the CATE function, shown to be non-pathwise differentiable, along with its piecewise linear approximation.
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Mind the Gap: Optimal and Equitable Encouragement Policies
Develops optimal encouragement policies distinguishing responsiveness from efficacy, targeting induced take-up for fairness under budget constraints in non-adherence settings.
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Fast convergence rates for dose-response estimation
Develops m-th order estimators for dose-response functions based on higher-order influence functions that attain the fastest known convergence rates under stated conditions.
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Generating Plausible Stress Scenarios via Large Deviations
A large-deviations method generates plausible stress scenarios for financial losses by concentrating on most likely configurations conditional on large losses, recovering stressed loss laws even with sparse data.