analyze-drivers

Use the shared contract and filter/unit reference. Reuse metadata and frozen baseline scope; choose a specific defined outcome and relevant case-level attributes from the user's context, without a candidate quota. If none is known, ask; do not sweep. State a testable hypothesis, possible competing explanation and what would distinguish them. Load the investigation method for evidence/status tracking. For utilization, first check timestamp imports and the zero-duration convention in the unit reference. A cohort-mix convention is not resource efficiency or measured productive time.

1. Check A's metadata level before correlation. Reuse a cached signal instead of repeating it. get_correlation_v2 {S,attributeName:A,influenceFormula:I,sortOrder:"Descending",filterOptions:F}.

   Outcome	I
   Duration / waiting / active time	DurationInfluence / WaitingTimeInfluence / ActiveTimeInfluence
   Utilization / event count	CaseUtilizationInfluence / EventCountInfluence
   Rework / loops / self-loops	ReworkCountInfluence / LoopCountInfluence / SelfloopCountInfluence

   Use the advertised enum and exact spelling. There are no itemsPerPage, itemsToSkip, top, metrics or limit arguments. The service ranks at most 20 groups.

2. Only if prevalence or mean evidence is needed and statistics is advertised: get_attribute_statistics {S,attributeName:A,filterOptions:F,itemsPerPage:5,itemsToSkip:0,metricToSortBy:"CaseFrequency",sortOrder:"Descending"}. Match returned values explicitly. Influence-ranked groups may differ from frequency-ranked groups; an absent match is unknown, not zero.

3. For a remaining hypothesis, use a discriminating matched comparison via compare-cohorts, focused statistics/cases, or another relevant candidate. Derive each test from frozen F, never an outcome-only exploratory subset. A cached signal can go directly to its test. Continue needed checks/pages without fixed analytical-call or candidate quotas. If matched evidence contradicts the proposed direction, mark it contradicted within that scope; do not privilege the earlier influence score or claim it proves a cause.

An event-level Resource cannot be sent to correlation. If useful and advertised, use the statistics call above for that attribute, but describe grouped event duration/frequencies, not case-duration drivers. Otherwise ask for a case-level candidate or report the capability gap. Do not silently substitute a different causal analysis.

Influence is a prevalence-weighted descriptive score, not Pearson correlation, significance, causal effect or confidence. Its formula already scales by 100; do not multiply again. Return at most five useful signals with available counts/means, units, scope and partial coverage. Label explanations as hypotheses, note selection/confounding, and state one falsifiable evidence check. Record tested versus merely observed claims, alternatives and missing evidence; revise or stop when the next check cannot discriminate. No assumed outcome maturity or closure. Stop when no discriminating evidence remains; do not paginate correlations or invent sample sizes.