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Due to the imbalanced nature of networked observational data, the causaleffect predictions for some individuals can severely violate thepositivity/overlap assumption, rendering unreliable estimations. Nevertheless,this potential risk of individual-level treatment effect estimation onnetworked data has been largely under-explored. To create a more trustworthycausal effect estimator, we propose the uncertainty-aware graph deep kernellearning (GraphDKL) framework with Lipschitz constraint to model the predictionuncertainty with Gaussian process and identify unreliable estimations. To thebest of our knowledge, GraphDKL is the first framework to tackle the violationof positivity assumption when performing causal effect estimation with graphs.With extensive experiments, we demonstrate the superiority of our proposedmethod in uncertainty-aware causal effect estimation on networked data.

To Predict or to Reject: Causal Effect Estimation with Uncertainty on Networked Data