Mechanistic interpretability researchers bring causality theory to LLM analysis

A group of researchers is applying causal inference techniques to understand how large language models actually reason internally, moving beyond black-box approaches. For indie AI builders, this work could eventually yield tools to debug model behavior and build more predictable systems without relying solely on empirical trial-and-error.