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.
Econometrica: Journal of the Econometric Society , pages=
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Causal stability selection identifies treatment effect modifiers with a non-asymptotic bound on expected false positives by integrating cross-fitted CATE estimation and stability selection.
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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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Causal Stability Selection
Causal stability selection identifies treatment effect modifiers with a non-asymptotic bound on expected false positives by integrating cross-fitted CATE estimation and stability selection.