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The normal equations for least-squares are ∇βL(µ, β) =−2·X ⊤ y−µ1 n −Xβ = 0,(44) ∇µL(µ, β) =−2·1 ⊤ n y−µ1 n −Xβ = 0 · 1975

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math.ST 1

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Design-based finite-sample analysis for regression adjustment

math.ST · 2025-11-19 · unverdicted · novelty 6.0

Provides finite-sample valid, instance-adaptive confidence intervals for regression-adjusted ATE estimators under complete randomization in high-dimensional regimes using martingale and exchangeable-pair techniques.

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  • Design-based finite-sample analysis for regression adjustment math.ST · 2025-11-19 · unverdicted · none · ref 1

    Provides finite-sample valid, instance-adaptive confidence intervals for regression-adjusted ATE estimators under complete randomization in high-dimensional regimes using martingale and exchangeable-pair techniques.