Work

Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation

Year: 2025

Type: conference-paper

Abstract: Causal effect estimation (CEE) provides a crucial tool for predicting the unobserved counterfactual outcome for an entity. As CEE relaxes the requirement for "perfect'' counterfactual samples (e.g., patients with identical attributes and only differ in treatments received) that are impractical to obtain and can instead operate on observational data, it is usually used in high-stake domains like medical treatment effect prediction. Nevertheless, in those high-stake domains, gathering a decently sized, fully labelled observational dataset remains challenging due to hurdles associated with costs, ethics, expertise and time needed, etc., of which medical treatment surveys are a typical example. Consequently, if the training dataset is small in scale, low generalization risks can hardly be achieved on any CEE algorithms.

Source: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

DOI: 10.1145/3690624.3709305

Authors: Hechuan Wen, Tong Chen, Guanhua Ye, Li Kheng Chai, Shazia Sadiq, Hongzhi Yin

Institutions: The University of Queensland, Beijing University of Posts and Telecommunications, Health and Wellbeing Queensland

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: Australian Research Council

Award: Decentralised Collaborative Predictive Analytics on Personal Smart Devices