A semiparametric debiased ML estimator for conditional means from aggregate data, with sensitivity analysis and a nonparametric test for the identifying assumption.
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stat.ME 2years
2025 2verdicts
UNVERDICTED 2representative citing papers
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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Identification and Semiparametric Estimation of Conditional Means from Aggregate Data
A semiparametric debiased ML estimator for conditional means from aggregate data, with sensitivity analysis and a nonparametric test for the identifying assumption.
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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.