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 …

Most works in causal inference focus on binary treatments where one estimates a single
treatment-versus-control effect. When treatment is continuous, one must estimate a curve …

In the domain of causal inference research, the prevalent potential outcomes framework,
notably the Rubin Causal Model (RCM), often overlooks individual interference and …

In this paper we introduce a variant of optimal transport adapted to the causal structure given
by an underlying directed graph. Different graph structures lead to different specifications of …

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 …

In this paper, we introduce a variant of optimal transport adapted to the causal structure
given by an underlying directed graph $ G $. Different graph structures lead to different …

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 …

After China's accession to the WTO and 20 years of rapid development, the marriage rate
has shown a downward trend. This study aims to analyze the impact of socio-economic …

Deep learning (DL) has received increasing attention lately as a promising framework for
clinical applications in healthcare. However, many existing DL works focus on making …