To Predict or to Reject: Causal Effect Estimation with Uncertainty on Networked Data
预测还是拒绝:网络数据中具有不确定性评估的因果效应估计
Due to the imbalanced nature of networked observational data, the causal effect predictions for some individuals can severely violate the positivity/overlap assumption, rendering unreliable estimations. Nevertheless, this potential risk of individual-level treatment effect estimation on networked data has been largely under-explored. To create a more trustworthy causal effect estimator, we propose the uncertainty-aware graph deep kernel learning (GraphDKL) framework with…