Estimating treatment effects from observational data is often subject to a covariate shift
problem incurred by selection bias. Recent research has sought to mitigate this problem by …

Consumer credit services offered by electronic commerce platforms provide customers with
convenient loan access during shopping and have the potential to stimulate sales. To …

Estimating the average treatment effect (ATE) from observational data is challenging due to
selection bias. Existing works mainly tackle this challenge in two ways. Some researchers …

Credit policy evaluation presents profitable opportunities for E-commerce platforms through
improved decision-making. The core of policy evaluation is estimating the causal effects of …

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 …

Causal learning is the key to obtaining stable predictions and answering\textit {what if}
problems in decision-makings. In causal learning, it is central to seek methods to estimate …

Estimating treatment effects from observational data is subject to a problem of covariate shift
caused by selection bias. Recent studies have attempted to mitigate this problem by group …