Causal Discovery in the Era of Agents

The article introduces "causal-learn+", an online platform designed to enhance causal discovery workflows by integrating agent assistance without compromising data integrity. It emphasizes that while agents can aid in data inspection and method explanation, they should not influence causal conclusions directly. This approach aims to maintain the rigor of causal analysis by grounding claims in data and formal algorithms, thus providing practitioners with a more reliable framework for causal inference in AI applications.