Robust and efficient multiple-unit switchback experimentation

TL;DR

This paper introduces Regular Balanced Switchback Designs (RBSDs), a new class of experimental designs that improve the accuracy of causal effect estimates in online experiments where user randomization is infeasible.

Contribution

The paper proposes RBSDs, a novel design framework that enhances empirical performance and robustness in item-randomized experiments, especially under carryover effects.

Findings

RBSDs outperform standard item-randomized designs in simulations.

RBSDs provide more accurate causal estimates in real e-commerce data.

RBSDs are resilient to carryover effects.

Abstract

User-randomized A/B testing has emerged as the gold standard for online experimentation. However, when this kind of approach is not feasible due to legal, ethical or practical considerations, experimenters have to consider alternatives like item-randomization. Item-randomization is often met with skepticism due to its poor empirical performance. To fill this gap, in this paper we introduce a novel and rich class of experimental designs, "Regular Balanced Switchback Designs" (RBSDs). At their core, RBSDs work by randomly changing treatment assignments over both time and items. After establishing the properties of our designs in a potential outcomes framework, characterizing assumptions and conditions under which corresponding estimators are resilient to the presence of carryover effects, we show empirically via both realistic simulations and real e-commerce data that RBSDs systematically outperform standard item-randomized and non-balanced switchback approaches by yielding much more accurate estimates of the causal effects of interest without incurring any additional bias.