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.

翻译：由于网络化观测数据的不平衡特性，某些个体的因果效应预测可能严重违反正向/重叠假设，导致不可靠的估计。然而，网络数据中个体处理效应估计的潜在风险尚未得到充分探索。为构建更可信的因果效应估计器，我们提出了一种具有Lipschitz约束的不确定性感知图深度核学习（GraphDKL）框架，通过高斯过程对预测不确定性进行建模，并识别不可靠估计。据我们所知，GraphDKL是首个在处理图数据因果效应估计时解决正定性假设违背问题的框架。通过大量实验，我们证明了所提方法在网络数据不确定性感知因果效应估计中的优越性。