Many research questions in Earth and environmental sciences are inherently causal,
requiring robust analyses to establish whether and how changes in one variable cause …

This comprehensive survey explored the evolving landscape of generative Artificial
Intelligence (AI), with a specific focus on the transformative impacts of Mixture of Experts …

Recently, recommender system (RS) based on causal inference has gained much attention
in the industrial community, as well as the states of the art performance in many prediction …

Causal inference has recently garnered significant interest among recommender system
(RS) researchers due to its ability to dissect cause-and-effect relationships and its broad …

Learning about cause and effect is arguably the main goal in applied econometrics. In
practice, the validity of these causal inferences is contingent on a number of critical …

Abstract Purpose of Review Epidemiologic research is rarely based on a random sample of
a well-defined target population. We used causal directed acyclic graphs to demonstrate the …

Large language models (LLMs) can easily generate biased and discriminative responses.
As LLMs tap into consequential decision-making (eg, hiring and healthcare), it is of crucial …

Learning causal effects from observational data greatly benefits a variety of domains such as
health care, education, and sociology. For instance, one could estimate the impact of a new …

To improve user experience, recommender systems have been widely used on many online
platforms. In these systems, recommendation models are typically learned from …

Empirical researchers and decision-makers spanning various domains frequently seek
profound insights into the long-term impacts of interventions. While the significance of long …