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.
arXiv preprint arXiv:2109.10522 , year=
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Proposes a covariance-aware tuning-free shrinkage framework and sequential algorithm for multi-source estimation that attains oracle risk asymptotically and improves on single-step methods.
ECO-ATE is a federated semiparametrically efficient estimator for the average treatment effect on a target population that incorporates summary statistics from source populations while allowing distributional shifts.
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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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Tuning-Free Efficient Estimation for Multi-Source Data via Covariance-Aware Shrinkage
Proposes a covariance-aware tuning-free shrinkage framework and sequential algorithm for multi-source estimation that attains oracle risk asymptotically and improves on single-step methods.
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Efficient collaborative learning of the average treatment effect
ECO-ATE is a federated semiparametrically efficient estimator for the average treatment effect on a target population that incorporates summary statistics from source populations while allowing distributional shifts.