A context graph could capture the full context, reasoning, and causal relationships behind critical business decisions. It’s a powerful idea. A December 2025 paper from Silicon Valley venture capital ...

Aim Causal inference relies on correct background knowledge, which epidemiologists generally understand to come from academic experts. Our community-engaged study augments scientific domain knowledge ...

The term evidence-based medicine, coined by Dr. Guyatt in 1991 (1), describes the practice of medicine rooted in the best available scientific evidence (2). Since its inception, evidence-based ...

Deciding which concepts should be described in causal language and which should not Box 1 contains a short fictional dialogue demonstrating why causal research questions cannot be articulated using ...

This project demonstrates how to build a complete agentic knowledge graph system using Kuzu, a powerful lightweight, embeddable graph database that can be integrated directly inside AI systems without ...

Abstract: Algorithmic recourse (AR) has made significant progress by identifying small perturbations in input features that can alter predictions, which provide a data-centric approach to understand ...

We study a contextual bandit setting where the agent has access to causal side information, in addition to the ability to perform multiple targeted experiments corresponding to potentially different ...

In an era where data-driven decision-making dominates the business landscape, traditional AI has excelled at predicting outcomes based on past occurrences. Yet, as our challenges grow in complexity, ...

While Generative AI has sparked great excitement, a form of artificial intelligence called Causal AI might offer much greater potential. Causal AI offers the tantalizing promise of being able to ...