Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.
and Brooks, Maria M
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A semiparametric sensitivity analysis framework is proposed for estimating comprehensive cohort causal effects in mixed RCT-OBS designs with unmeasured confounding and missing-at-random outcomes.
The paper formalizes identification strategies for potential outcome means and average treatment effects when merging experimental studies with external data sources.
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Private Rate-Double-Robust Inference
Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.
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Inferring Comprehensive Cohort Causal Effects in the Presence of Unmeasured Confounding and Missing Outcomes
A semiparametric sensitivity analysis framework is proposed for estimating comprehensive cohort causal effects in mixed RCT-OBS designs with unmeasured confounding and missing-at-random outcomes.
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Identification strategies for combining an experimental study with external data
The paper formalizes identification strategies for potential outcome means and average treatment effects when merging experimental studies with external data sources.