Near-Optimal Experimental Design Under the Budget Constraint in Online Platforms

TL;DR

This paper proposes an optimal experimental design for A/B testing on two-sided online platforms with budget constraints, reducing bias and variance compared to traditional methods, validated through synthetic and real data.

Contribution

It introduces a novel model for budget-constrained buyers on two-sided platforms and provides an optimal design that minimizes bias and variance in experiments.

Findings

Design reduces bias with increased budgets and supply-demand rate

Outperforms Bernoulli randomization in experiments

Validated on synthetic and real-world data

Abstract

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.