1 citations · 1 across the 8 of their papers we have counts for
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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…
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…
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…
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…