When estimating causal effects from observational studies, researchers often need to adjust
for many covariates to deconfound the non-causal relationship between exposure and …

In many real-world causal inference applications, the primary outcomes (labels) are often
partially missing, especially if they are expensive or difficult to collect. If the missingness …

Data integration has become increasingly common in aligning multiple heterogeneous
datasets. With high-dimensional outcomes, data integration methods aim to extract low …

The estimation of exposure effects in observational studies is often complicated due to
confounding factors, particularly in high-dimensional'omics data. The traditional regression …