Introduces a nonparametric identification condition using cyclic monotonicity of the first stage for multivariate IV models with binary instruments, with a corrigendum updating the proof to inverse Brenier maps for more flexible distributions.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4representative citing papers
Two steps of gradient descent on first-layer weights in linear-width two-layer networks produce a spiked random matrix with floor(alpha2/(1/2-alpha1)) outliers, each a learned direction, and batch reuse allows capturing directions with information exponent exceeding one.
A Neyman-orthogonal estimator paired with Lasso nuisance estimation achieves root-T asymptotic normality for BLP demand parameters under high-dimensional controls and approximate sparsity.
PLANE jointly estimates latent gene positions from a target network and proxy embeddings on a larger gene set, with provably optimal channel weighting and demonstrated gains in network recovery and imputation.
citing papers explorer
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A condition for the identification of multivariate models with binary instruments -- with Corrigendum and Addendum
Introduces a nonparametric identification condition using cyclic monotonicity of the first stage for multivariate IV models with binary instruments, with a corrigendum updating the proof to inverse Brenier maps for more flexible distributions.
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Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent
Two steps of gradient descent on first-layer weights in linear-width two-layer networks produce a spiked random matrix with floor(alpha2/(1/2-alpha1)) outliers, each a learned direction, and batch reuse allows capturing directions with information exponent exceeding one.
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Estimation of BLP models with high-dimensional controls
A Neyman-orthogonal estimator paired with Lasso nuisance estimation achieves root-T asymptotic normality for BLP demand parameters under high-dimensional controls and approximate sparsity.
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AI-Augmented Statistical Network Estimation with Proxy Gene Embeddings
PLANE jointly estimates latent gene positions from a target network and proxy embeddings on a larger gene set, with provably optimal channel weighting and demonstrated gains in network recovery and imputation.