Causal inference has numerous real-world applications in many domains, such as health
care, marketing, political science, and online advertising. Treatment effect estimation, a …

A central question in many fields of scientific research is to determine how an outcome is
affected by an action, ie, to estimate the causal effect or treatment effect of an action. In …

In causal inference, it is common to select a subset of observed covariates, named the
adjustment features, to be adjusted for estimating the treatment effect. For real-world …

Recent years have witnessed a rocketing growth of machine learning methods on graph
data, especially those powered by effective neural networks. Despite their success in …

Learning individual-level treatment effect is a fundamental problem in causal inference and
has received increasing attention in many areas, especially in the user growth area which …

Causality lays the foundation for the trajectory of our world. Causal inference (CI), which
aims to infer intrinsic causal relations among variables of interest, has emerged as a crucial …

Estimating individual treatment effect (ITE) from observational data is a crucial but
challenging task. Disentangled representations have been used to separate proxy variables …

As an important problem in causal inference, we discuss the identification and estimation of
treatment effects (TEs) under limited overlap; that is, when subjects with certain features …

A causal effect can be defined as a comparison of outcomes that result from two or more
alternative actions, with only one of the action-outcome pairs actually being observed. In …

Causal learning has attracted much attention in recent years because causality reveals the
essential relationship between things and indicates how the world progresses. However …