Polymers are ubiquitous to almost every aspect of modern society and their use in medical
products is similarly pervasive. Despite this, the diversity in commercial polymers used in …

Deep learning relies on learning from extensive data to generate prediction results. This
approach may inadvertently capture spurious correlations within the data, leading to models …

Graph-structured data are pervasive in the real-world such as social networks, molecular
graphs and transaction networks. Graph neural networks (GNNs) have achieved great …

Emergence and causality are two fundamental concepts for understanding complex
systems. They are interconnected. On one hand, emergence refers to the phenomenon …

Decision support systems based on machine learning models should be able to help users
identify opportunities and threats. Popular model-agnostic explanation models can identify …

This paper aims to present a methodology for the application of matching methods in
industry to measure causal effect size. Matching methods allow us to obtain treatment and …

This paper proposes causal economic machine learning (CEML) as a research agenda that
utilizes causal machine learning (CML), built on causal economics (CE) decision theory …

Causal inference has become an important research direction in the field of computing.
Traditional methods have mainly used Bayesian networks to discover the causal effects …