Causal model
A structural model inferred from co-occurrence in experience history that represents directional relationships between operational events. Supports counterfactual reasoning and intervention evaluation.
The causal model is what distinguishes the system's understanding of its environment from a purely correlational pattern library. A pattern library can record that backpressure accumulation and tail latency expansion tend to co-occur; the causal model represents the directional claim that backpressure causes latency expansion, not the reverse. This directionality is what makes intervention reasoning possible: if A causes B, then intervening on A is the lever; if we incorrectly believed B causes A, we would intervene on the wrong thing. The causal model is inferred from experience history by looking for temporal precedence and ruling out confounders - events that co-occur because they share a common upstream cause rather than because one produces the other. It supports counterfactual queries: given that we observed outcome O, what would have happened if we had taken action A instead of action B? The grounding engine continuously validates causal model predictions against observed telemetry, correcting directional claims when the observed data contradicts the model's structure.