To Predict or to Reject: Causal Effect Estimation with Uncertainty on Networked Data
This work addresses a critical issue in causal inference for networked data, offering a more trustworthy estimator for applications like social network analysis or recommendation systems, though it is incremental as it builds on existing graph and kernel methods.
The paper tackles the problem of unreliable causal effect predictions on networked data due to violations of the positivity assumption, proposing an uncertainty-aware graph deep kernel learning framework that identifies unreliable estimations and demonstrates superiority in experiments.
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.