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Found 2 papers, 1 papers with code
A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testing
The reliability of controlled experiments, or "A/B tests," can often be compromised due to the phenomenon of network interference, wherein the outcome for one unit is influenced by other units.
Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests
Our study provides an approach that accounts for both the local structure in a user's social network via motifs as well as the assignment conditions of neighbors.
Social and Information Networks Applications
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