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20232026
most citedOptimized Covariance Design for AB Test on Social Network under Interference

1 citations · 1 across the 9 of their papers we have counts for

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5 papers · 1 filter

stat.ME2026

Causal Estimation of Share-Induced Engagement with Flywheel Effects

Weitao Cheng, Yilin Li, Yong Wang +1

Sustainable user growth in online platforms depends not only on acquiring new users but also on reactivating and engaging existing ones through social sharing features. A well-desi…

stat.ME2026

Design-based edge-level causal inference with machine learning assisted covariate adjustment

Haoyang Yu, Yilin Li, Lu Deng +3

We study design-based causal inference for edge-level outcomes in directed networks under dyadic interference. In this setting, outcomes are defined on directed edges and depend on…

stat.ME2026

Journey to the Centre of Cluster: Harnessing Interior Nodes for A/B Testing under Network Interference

Qianyi Chen, Anpeng Wu, Bo Li +2

A/B testing on platforms often faces challenges from network interference, where a unit's outcome depends not only on its own treatment but also on the treatments of its network ne…

stat.ME2025

Causal inference with dyadic data in randomized experiments

Yilin Li, Lu Deng, Yong Wang +1

Estimating treatment effects in networked settings is a central challenge in online controlled experiments, particularly on social media platforms. We investigate a scenario where…

stat.ME20231 cited

Optimized Covariance Design for AB Test on Social Network under Interference

Qianyi Chen, Bo Li, Lu Deng +1

Online A/B tests have become increasingly popular and important for social platforms. However, accurately estimating the global average treatment effect (GATE) has proven to be cha…