Treatment Effect Estimation with Differentiated Networked Effect on Graph Data 事件

Treatment Effect Estimation with Differentiated Networked Effect on Graph Data arXiv:2605.24358v1 Announce Type: cross Abstract: Estimating individual treatment effect (ITE) from observational graph data is crucial for decision-making in the fields such as commerce and medicine. This task is challenging due to interference, where individual outcomes can be influenced by the treatments and covariates of their neighbors. Existing methods attempt to model such interference for accurate ITE estimat