Congyi Zhou | Searcharxiv

arXiv2026

Generative AI in Action: Field Experimental Evidence from Alibaba's Customer Service Operations

In collaboration with Alibaba, we study how a generative AI assistant affects service performance in e-commerce after-sales operations. In a large-scale field experiment, human agents providing digital chat support were randomly assigned access to a gen AI assistant. The assistant drafts issue diagnoses and solution proposals in the opening stage only; agents can adopt, modify, or disregard them. Because of this discretion, we estimate the effects of both gen AI access and usage. On average, gen AI improves service speed and subjective service quality, measured by customer ratings, but has no significant effect on objective service quality, measured by customer retrials. These gains come from more than automation. Gen AI reshapes agent-customer interactions: treated agents respond faster and take a more proactive role, while customers provide less input; both patterns persist into later chat stages. These average effects, however, mask heterogeneity across agents. Lower-performing agents benefit the most, indicating that gen AI can narrow performance gaps. Top-performing agents experience declines in both subjective and objective service quality. This decline is consistent with workflow disruption: among top performers, gen AI use increases shift-away time, response delays, and immediate customer retrials, suggesting weakened service continuity in the focal chat. Successful gen AI deployment therefore requires careful performance evaluation and rollout tailored to agent skill.

cs.HC↗

arXiv2025

Unbiased Platform-Level Causal Estimation for Search Systems: A Competitive Isolation PSM-DID Framework

Evaluating platform-level interventions in search-based two-sided marketplaces is fundamentally challenged by systemic effects such as spillovers and network interference. While widely used for causal inference, the PSM (Propensity Score Matching) - DID (Difference-in-Differences) framework remains susceptible to selection bias and cross-unit interference from unaccounted spillovers. In this paper, we introduced Competitive Isolation PSM-DID, a novel causal framework that integrates propensity score matching with competitive isolation to enable platform-level effect measurement (e.g., order volume, GMV) instead of item-level metrics in search systems. Our approach provides theoretically guaranteed unbiased estimation under mutual exclusion conditions, with an open dataset released to support reproducible research on marketplace interference (github.com/xxxx). Extensive experiments demonstrate significant reductions in interference effects and estimation variance compared to baseline methods. Successful deployment in a large-scale marketplace confirms the framework's practical utility for platform-level causal inference.

cs.AI↗