arXiv:2403.17285v1 Announce Sort: new
Summary: This paper presents an in depth investigation of switchback designs in A/B testing, which alternate between baseline and new insurance policies over time. Our purpose is to completely consider the consequences of those designs on the accuracy of their ensuing common remedy impact (ATE) estimators. We suggest a novel “weak sign evaluation” framework, which considerably simplifies the calculations of the imply squared errors (MSEs) of those ATEs in Markov determination course of environments. Our findings counsel that (i) when the vast majority of reward errors are positively correlated, the switchback design is extra environment friendly than the alternating-day design which switches insurance policies in a day by day foundation. Moreover, growing the frequency of coverage switches tends to cut back the MSE of the ATE estimator. (ii) When the errors are uncorrelated, nonetheless, all these designs turn into asymptotically equal. (iii) In circumstances the place the vast majority of errors are adverse correlated, the alternating-day design turns into the optimum alternative. These insights are essential, providing tips for practitioners on designing experiments in A/B testing. Our evaluation accommodates a wide range of coverage worth estimators, together with model-based estimators, least squares temporal distinction studying estimators, and double reinforcement studying estimators, thereby providing a complete understanding of optimum design methods for coverage analysis in reinforcement studying.
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