Work

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

Year: 2023

Type: conference-paper

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. The code of GraphDKL is available at https://github.com/uqhwen2/GraphDKL.

Source: Proceedings

DOI: 10.1109/icdm58522.2023.00184

Authors: Hechuan Wen, Tong Chen, Li Kheng Chai, Shazia Sadiq, Kai Zheng, Hongzhi Yin

Institutions: The University of Queensland, Health and Wellbeing Queensland, University of Electronic Science and Technology of China

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: Australian Research Council

Awards: Decentralised Collaborative Predictive Analytics on Personal Smart Devices, Scalable and Lightweight On-Device Recommender Systems, Challenging big data for scalable, robust and real-time recommendations