Towards Measuring Sell Side Outcomes in Buy Side Marketplace Experiments using In-Experiment Bipartite Graph

TL;DR

This paper introduces a novel method for constructing bipartite graphs from in-experiment data to evaluate causal effects in online marketplace experiments, enabling more accurate seller-side outcome measurement.

Contribution

The study presents a new approach to building bipartite graphs directly from experimental data, advancing causal inference in marketplace experiments without relying on prior or historical data.

Findings

Effective bipartite graph construction from in-experiment data

Application to real-world marketplace data at Vinted

Enhanced measurement of seller-side outcomes

Abstract

In this study, we evaluate causal inference estimators for online controlled bipartite graph experiments in a real marketplace setting. Our novel contribution is constructing a bipartite graph using in-experiment data, rather than relying on prior knowledge or historical data, the common approach in the literature published to date. We build the bipartite graph from various interactions between buyers and sellers in the marketplace, establishing a novel research direction at the intersection of bipartite experiments and mediation analysis. This approach is crucial for modern marketplaces aiming to evaluate seller-side causal effects in buyer-side experiments, or vice versa. We demonstrate our method using historical buyer-side experiments conducted at Vinted, the largest second-hand marketplace in Europe with over 80M users.