How 2025 Recalibrated AI Models Race

In 2025, large language models moved beyond benchmarks to efficiency, reliability, and integration, reshaping how AI is ...
BMJ Global Health

Maternal and child healthcare-seeking among victims of violence in armed conflict: a quasi-experimental study in Northeast Nigeria

Introduction Armed conflict severely impacts health, with indirect deaths often exceeding direct casualties two to four times ...
Scientific Research Publishing

Hill, J.L. (2011) Bayesian Nonparametric Modeling for Causal Inference. Journal of Computational and Graphical Statistics, 20, 217-240.

ABSTRACT: Determining the causal effect of special education is a critical topic when making educational policy that focuses on student achievement. However, current special education research is ...
GitHub

graphical-models

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical ...
IEEE

Uncertain Priors for Graphical Causal Models: a Multi-objective Optimization Perspective

Abstract: Learning graphical causal models from observational data can effectively elucidate the underlying causal mechanism behind the variables. In the context of limited datasets, modelers often ...
SiliconANGLE

Data Poem launches AI ‘large causal model’ to help businesses drive mission-critical operations

Data Poem, a developer of cutting-edge artificial intelligence models for business orchestration, today announced the launch of POEM365, a large causal AI model designed to transform how large ...
EurekAlert!

Offline model-based reinforcement learning with causal structured world models

The architecture of FOCUS. Given offline data, FOCUS learns a $p$ value matrix by KCI test and then gets the causal structure by choosing a $p$ threshold. After ...