In machine learning, exploring data correlations to predict outcomes is a fundamental task.
Recognizing causal relationships embedded within data is pivotal for a comprehensive …

Causal inference is a critical technique for inferring causal relationships from data and
distinguishing causation from correlation. Causal inference frameworks rely on structured …

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

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 …

Real-world multi-agent systems are often dynamic and continuous, where the agents co-
evolve and undergo changes in their trajectories and interactions over time. For example …

Causal effect estimation under networked interference is an important but challenging
problem. Available parametric methods are limited in their model space, while previous …

Existing causal models for link prediction assume an underlying set of inherent node factors—
an innate characteristic defined at the node's birth—that governs the causal evolution of …

Estimating causal effects is crucial for decision-makers in many applications, but it is
particularly challenging with observational network data due to peer interactions. Many …

Estimating individual treatment effects in networked observational data is a crucial and
increasingly recognized problem. One major challenge of this problem is violating the stable …

Causal inference on networks faces challenges posed in part by violations of standard
identification assumptions due to dependencies between treatment units. Although graph …