Causal Panopticon
Empower AI agents with advanced causal discovery and inference capabilities across diverse data domains.
Empower AI agents with advanced causal discovery and inference capabilities across diverse data domains.
Causal Panopticon is a sophisticated cross-domain engine designed for AI agents, offering robust causal discovery and inference. Exposed via the Model Context Protocol, it seamlessly integrates data from 18 heterogeneous sources spanning economics, health, environment, security, policy, finance, academia, and labor. By applying 8 peer-reviewed causal algorithms, this tool moves beyond mere correlation to uncover the actual causal structures between variables. It's an essential resource for researchers, policy analysts, and AI agents needing to obtain structured inference results, including average treatment effects, counterfactual values, and validated causal graphs, all through a single MCP connection.