Causal Safety Engine is a safety-first validation and governance layer designed for AI agents and autonomous decision systems.
It does not aim to discover new causal relationships for optimization or intervention. Instead, it focuses on blocking unsafe, non-robust, or non-identifiable causal signals before they are used to justify autonomous actions.
The engine is intended to be used as a guardrail, not as a decision-maker.
Modern AI agents increasingly rely on correlations or weak causal signals to justify actions in high-risk environments.
This creates three major risks:
Causal Safety Engine addresses these risks by enforcing strict causal and epistemic constraints before any signal can be considered actionable.
If causal evidence is insufficient, the engine deliberately produces no approval signal.
The engine evaluates directed predictive influence under strict constraints, including:
Signals that fail any invariant are rejected or flagged.
Its sole purpose is risk reduction and validation.
It is designed to be integrated as an independent safety layer in existing AI systems.
Contributions, reviews, and audits are welcome.