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

• URL: http://arxiv.org/abs/2309.08165v1
• Date: Fri, 15 Sep 2023 05:25:43 GMT
• Title: To Predict or to Reject: Causal Effect Estimation with Uncertainty on Networked Data
• Authors: Hechuan Wen, Tong Chen, Li Kheng Chai, Shazia Sadiq, Kai Zheng, Hongzhi Yin
• Abstract summary: 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.
• Score: 36.31936265985164
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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