OpenCM 1.0
The Universal Interchange Standard for Causal AI.
Causal AI — and We’re Giving It Away
The Infrastructure Gap in Causal AI
The year is 2026. Causal AI is finally having its moment. After decades of Judea Pearl’s pioneering work on causal inference and structural causal models (SCMs), enterprises are waking up to a fundamental truth: correlation is cheap, causation is priceless.
But causal inference has no portable model format. OpenCM does for causal inference what ONNX did for neural networks: it makes causal models portable, versionable, composable, and transparent.
Model Registries
Maintain validated causal models—Porter’s Five Forces, PESTLE, and more. Load instantly into your engine.
The Lensing Engine
Treat models like interchangeable reasoning overlays. Swap causal worldviews as easy as changing camera lenses.
Standardized JSON
Language-agnostic specification for variables, edges, structural equations, and explicit assumptions.