A unified robust framework for sequential A/B testing bounds the worst-case mean squared error of treatment effect estimates under model misspecification in both contextual bandit and dynamic regimes.
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3 Pith papers cite this work. Polarity classification is still indexing.
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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.
A unified least squares framework for identifying and estimating causal effects in crossover designs that remains valid under misspecified working models.
citing papers explorer
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Robust Sequential Experimental Design for A/B Testing
A unified robust framework for sequential A/B testing bounds the worst-case mean squared error of treatment effect estimates under model misspecification in both contextual bandit and dynamic regimes.
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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.
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Principled analysis of crossover designs: causal effects, efficient estimation, and robust inference
A unified least squares framework for identifying and estimating causal effects in crossover designs that remains valid under misspecified working models.