See whether an intervention changed the outcome.
CausalGraphOS brings a business question, relevant data, and the assumptions behind an analysis into one place. It helps teams see what the evidence supports, and where it falls short.
From a business question to a reviewable answer.
Start with the decision your team is facing. Bring the data and domain knowledge together, explore possible reasons for the outcome, and record the assumptions behind each explanation.
When the data supports it, analysts can estimate how an intervention may affect an outcome, compare it with what might have happened without that action, and check whether the result changes under different assumptions. A correlation, a possible explanation, and a supported effect are different things.
Keep findings, limits, and decisions together so the team can review them. People remain responsible for interpreting the evidence and approving consequential decisions.
Use your team’s assistant or connect through APIs.
With MCP, assistants such as Claude and ChatGPT can work with approved, read-only CausalGraphOS case context. A2A lets agents share progress and hand work off. APIs provide direct connections for applications and services. All are supported ways to work with CausalGraphOS.