1 citations · 1 across the 8 of their papers we have counts for
8 papers
Doubly Robust Estimation of Causal Effect on CVR with Targeted Regularization
Jiayi Dan, Bo Li, Lu Deng +1
Post-click conversion rate (CVR) is a key metric in various scenarios including e-commerce and advertising, reflecting the efficiency and user experience in the second stage of the…
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…
Causal Inference in Social Platforms Under Approximate Interference Networks
Yiming Jiang, Lu Deng, Yong Wang +1
Estimating the total treatment effect (TTE) of a new feature in social platforms is crucial for understanding its impact on user behavior. However, the presence of network interfer…
Ego Group Partition: A Novel Framework for Improving Ego Experiments in Social Networks
Lu Deng, JingJing Zhang, Yong Wang +1
Estimating the average treatment effect in social networks is challenging due to individuals influencing each other. One approach to address interference is ego cluster experiments…
Unbiased Estimation for Total Treatment Effect Under Interference Using Aggregated Dyadic Data
Lu Deng, Yilin Li, JingJing Zhang +2
In social media platforms, user behavior is often influenced by interactions with other users, complicating the accurate estimation of causal effects in traditional A/B experiments…
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…