Near-Optimal Experimental Design Under the Budget Constraint in Online Platforms
This addresses a practical problem for online platforms with budget-limited buyers, offering an incremental improvement over existing A/B testing methods.
The paper tackles the challenge of conducting A/B testing on two-sided platforms with budget constraints, where conventional methods may cause buyers to exceed their limits, and develops an optimal experimental design that reduces bias and variance, validated on synthetic and real-world data.
A/B testing, or controlled experiments, is the gold standard approach to causally compare the performance of algorithms on online platforms. However, conventional Bernoulli randomization in A/B testing faces many challenges such as spillover and carryover effects. Our study focuses on another challenge, especially for A/B testing on two-sided platforms -- budget constraints. Buyers on two-sided platforms often have limited budgets, where the conventional A/B testing may be infeasible to be applied, partly because two variants of allocation algorithms may conflict and lead some buyers to exceed their budgets if they are implemented simultaneously. We develop a model to describe two-sided platforms where buyers have limited budgets. We then provide an optimal experimental design that guarantees small bias and minimum variance. Bias is lower when there is more budget and a higher supply-demand rate. We test our experimental design on both synthetic data and real-world data, which verifies the theoretical results and shows our advantage compared to Bernoulli randomization.