QBHM estimator matches standard GMM asymptotics for strongly identified parameters and is a Bayes rule under squared loss in weak-GMM limit experiments induced by the hierarchy.
arXiv preprint arXiv:2510.22864 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Proposes a Markovian framework for N-of-1 experimental design and establishes large-T asymptotic optimality results for random-switch and cycle-switch designs under finite-order impulse-response models.
A method estimates unsigned CATE from residual outcome covariances to assist randomization tests without sample splitting, establishing identification, consistency, and validity while showing higher power in simulations.
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Quasi-Bayesian Hierarchical Models
QBHM estimator matches standard GMM asymptotics for strongly identified parameters and is a Bayes rule under squared loss in weak-GMM limit experiments induced by the hierarchy.
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Experimental Design When N Equals One
Proposes a Markovian framework for N-of-1 experimental design and establishes large-T asymptotic optimality results for random-switch and cycle-switch designs under finite-order impulse-response models.
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Fit CATE Once: Model-Assisted Randomization Tests Without Sample Splitting
A method estimates unsigned CATE from residual outcome covariances to assist randomization tests without sample splitting, establishing identification, consistency, and validity while showing higher power in simulations.