6 papers
Wasserstein Policy Learning for Distributional Outcomes
Yiyan Huang, Cheuk Hang Leung, Qi Wu +1
Offline policy learning has received growing attention in causal inference. The primary objective is to learn a policy (individualized treatment rule) as a mapping from covariates…
Distribution-valued Causal Machine Learning: Implications of Credit on Spending Patterns
Cheuk Hang Leung, Yijun Li, Qi Wu
Fintech lending has become a central mechanism through which digital platforms stimulate consumption, offering dynamic, personalized credit limits that directly shape the purchasin…
Distributionally Robust Policy Evaluation and Learning for Continuous Treatment with Observational Data
Cheuk Hang Leung, Yiyan Huang, Yijun Li +1
Using offline observational data for policy evaluation and learning allows decision-makers to evaluate and learn a policy that connects characteristics and interventions. Most exis…
Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators
Yiyan Huang, Cheuk Hang Leung, Siyi Wang +2
The growing demand for personalized decision-making has led to a surge of interest in estimating the Conditional Average Treatment Effect (CATE). Various types of CATE estimators h…
SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization
Xixu Hu, Runkai Zheng, Jindong Wang +3
Vision Transformers (ViTs) are increasingly used in computer vision due to their high performance, but their vulnerability to adversarial attacks is a concern. Existing methods lac…
The Causal Impact of Credit Lines on Spending Distributions
Yijun Li, Cheuk Hang Leung, Xiangqian Sun +6
Consumer credit services offered by e-commerce platforms provide customers with convenient loan access during shopping and have the potential to stimulate sales. To understand the…