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.
On the role of the propensity score in efficient semiparametric estimation of average treatment effects.Econometrica, 66(2):315–331
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DR-ME is the first semiparametrically efficient finite-location kernel test for interpretable distributional treatment effects, using orthogonal doubly robust features derived from observational data.
Advocates applying causal inference to preference learning for LLM alignment to diagnose generalization failures and guide better data practices.
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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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Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
DR-ME is the first semiparametrically efficient finite-location kernel test for interpretable distributional treatment effects, using orthogonal doubly robust features derived from observational data.
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Preference Learning for AI Alignment: a Causal Perspective
Advocates applying causal inference to preference learning for LLM alignment to diagnose generalization failures and guide better data practices.