Methodology

The explanation engine analyzes stored metric history, then generates a narrative from the available analysis. The method that runs depends on the data and enabled capabilities. These are descriptions of implemented methods, not measured customer accuracy or causal guarantees.

Tier 1: Pattern Correlation Pearson correlation compares metric series aligned by date. It requires at least three shared observations, skips zero-variance series and returns up to ten relationships with absolute correlation above 0.5. These findings describe co-movement, not causation.

Tier 2: Temporal Analysis PELT changepoint detection uses a squared-error cost and BIC-style penalty to identify shifts in a metric series. Temporal analysis compares the order of detected shifts and their magnitude, with a minimum history requirement. The lead-time calculation assumes uniformly spaced observations. Temporal precedence alone does not establish a cause.

Tier 3: Quasi-Experimental Estimation Difference-in-differences and time-series baselines report estimated differences, statistical uncertainty and model assumptions. Reporting-window boundaries and synthetic split dates are not verified interventions: these comparisons remain observational even when statistically significant. They do not establish why a metric changed or validate a causal effect.

An optional counterfactual service can run time-series estimation when configured with sufficient history. Availability of an estimator is not evidence of an identified intervention or a supported causal claim.

Graph Discovery: Not Live in Explanations Graph discovery is not connected to customer-facing explanations. We do not claim graph-derived causal answers.

Data Requirements and Limits If the requested metric has insufficient stored history, the explanation engine returns an insufficient-data response rather than a generated explanation. Source data gaps, irregular sampling and estimator assumptions limit what analysis can establish. Review the underlying data and consider alternative explanations before acting. No customer accuracy percentages, outcome benchmarks or universal causal-answer guarantees are claimed here.