To Predict or to Reject: Causal Effect Estimation with Uncertainty on
  Networked Data
Hechuan Wen Tong Chen Li Kheng Chai Shazia Sadiq Kai Zheng Hongzhi Yin
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 Lipschitz constraint to model the prediction
uncertainty with Gaussian process and identify unreliable estimations. To the
best of our knowledge, GraphDKL is the first framework to tackle the violation
of positivity assumption when performing causal effect estimation with graphs.
With extensive experiments, we demonstrate the superiority of our proposed
method in uncertainty-aware causal effect estimation on networked data.