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

Physics-informed machine learning (PIML), referring to the combination of prior knowledge
of physics, which is the high level abstraction of natural phenomenons and human …

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 …

Causal effect estimation from networked observational data encounters notable challenges,
primarily hidden confounders arising from network structure, or spillover effects that …

Abstract Graph Neural Networks (GNNs) have emerged as the leading paradigm for solving
graph analytical problems in various real-world applications. Nevertheless, GNNs could …

In this paper, we study the causal effects estimation problem on networked observational
data. We theoretically prove that standard graph machine learning (ML) models, eg, graph …

Multi-agent dynamical systems refer to scenarios where multiple units (aka agents) interact
with each other and evolve collectively over time. For instance, people's health conditions …

Causal analysis for time series data, in particular estimating individualized treatment effect
(ITE), is a key task in many real world applications, such as finance, retail, healthcare, etc …

Explainable recommendation has recently attracted increasing attention from both academic
and industry communities. Among different explainable strategies, generating natural …

Time-aware recommendation has been widely studied for modeling the user dynamic
preference and a lot of models have been proposed. However, these models often overlook …