Service III
Training
Engineers who can build, test, and maintain dynamic causal models without outside help.
- The outcome
- The client's engineers learn to model, compose, and verify dynamic causal systems with DeepCausality, and to apply it to their own work. The course covers the Effect Propagation Process and its primitives, causal discovery with the Causal Discovery Language, composition with the Causal Flow Language, safety norms with the Effect Ethos, and verification.
- The evidence
- The course teaches from the DeepCausality project's published tutorials and worked examples, so every exercise runs and participants can check every result. The framework's author teaches it. A team can review the open material before booking.
- Time to first result
- A workshop runs two to three days. Participants run working examples in the first session. In the final session, they apply the methods to a problem from the client's own systems.
- How to start?
- Training suits a team that will build or maintain dynamic causal models itself. Participants need Rust programming experience. The open-source tooling runs on a laptop. A planning call matches the course to the team's systems and selects the problem for the final session. Workshops run remotely unless otherwise agreed.
Starting an engagement
The client describes the problem in a few lines, and the Center replies to arrange the first call. Client data and client-specific results stay with the client; verified general methods return to the open DeepCausality project.