OrcheCause Agent: From Textual Knowledge to End-to-End Causal Inference

Abstract Causal agents have emerged as promising tools for automating causal analysis based on user queries. However, existing causal agent systems are often limited to a single causal task, limiting their ability to handle complex queries. In addition, they accept only numerical data as input, preventing the integration of domain knowledge expressed in natural language. To overcome these limitations, we propose the OrcheCause agent, a causal agent leveraging textual knowledge for end-to-end causal inference. Specifically, OrcheCause is designed to orchestrate a sequence of interrelated causal tasks in response to user queries. Furthermore, OrcheCause supports diverse data types—numerical as well as textual data—by extracting cause-effect pairs from the relevant sources and incorporating them into causal discovery (CD), thereby improving the performance of CD. OrcheCause also introduces a metric-based hyperparameter optimization framework for CD when ground-truth graphs are not available.