5 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…
Risk-Neutral Generative Networks
Zhonghao Xian, Xing Yan, Cheuk Hang Leung +1
We present a generative approach to price options and extract risk-neutral densities from the market. Specifically, we model the underlying log-returns on the time-to-maturity cont…
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…
Probabilistic Learning of Multivariate Time Series with Temporal Irregularity
Yijun Li, Cheuk Hang Leung, Qi Wu
Probabilistic forecasting of multivariate time series is essential for various downstream tasks. Most existing approaches rely on the sequences being uniformly spaced and aligned a…
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…