A doubly robust estimator is developed for quantile treatment effects on long-term outcomes by integrating randomized trial data with observational data under surrogate transportability, remaining consistent if either nuisance function is correctly estimated.
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The Sinkhorn treatment effect is a new entropic optimal transport measure of divergence between counterfactual distributions that admits first- and second-order pathwise differentiability, debiased estimators, and asymptotically valid tests for distributional treatment effects.
A recursive Riesz representer-based targeted minimum loss estimation procedure unifies asymptotically efficient estimation of causal estimands such as time-varying treatment effects and mediation effects.
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Double/debiased machine learning of quantile treatment effects on long-term outcomes in clinical trials
A doubly robust estimator is developed for quantile treatment effects on long-term outcomes by integrating randomized trial data with observational data under surrogate transportability, remaining consistent if either nuisance function is correctly estimated.
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The Sinkhorn treatment effect is a new entropic optimal transport measure of divergence between counterfactual distributions that admits first- and second-order pathwise differentiability, debiased estimators, and asymptotically valid tests for distributional treatment effects.
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