Causal inference is one of the most important and challenging aims in statistics and data science. Many fields, from clinical medicine to social sciences, strive to use empirical data to understand ...

Aviation safety has transformed dramatically over the past six decades, yet comprehensive analysis of historical accident data remains fragmented across disparate systems and formats. The NTSB ...

Reinforcement‑learning framework for multi‑turn conversational AI agents. StateSet Agents is a production‑oriented RL stack for training and serving LLM‑backed agents that improve through multi‑turn ...

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 ...

Growing evidence has indicated that the nutritional quality of dietary intake and alterations in blood metabolites were related to human brain activity. This study aims to investigate the causal ...

Early identification of neonatal jaundice (NJ) appears to be essential to avoid bilirubin encephalopathy and neurological sequelae. The interaction between gut microbiota and metabolites plays an ...

PyMC is a probabilistic programming library for Python that provides tools for constructing and fitting Bayesian models. It offers an intuitive, readable syntax that is close to the natural syntax ...