Audit aggregate metric reversal

What it's for

Prevents companywide metric rollups from implying the opposite of what happens within comparable teams, repositories, projects, services, or time cohorts.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "bootstrap_draws": 200,
  "observations": [
    {
      "exposure": 0,
      "id": "aggregate-reversal-0-0",
      "outcome": 0,
      "stratum_id": "team-0",
      "weight": 1
    },
    {
      "exposure": 1,
      "id": "aggregate-reversal-0-1",
      "outcome": 1,
      "stratum_id": "team-0",
      "weight": 2
    },
    {
      "exposure": 2,
      "id": "aggregate-reversal-0-2",
      "outcome": 2,
      "stratum_id": "team-0",
      "weight": 3
    },
    {
      "exposure": 3,
      "id": "aggregate-reversal-0-3",
      "outcome": 3,
      "stratum_id": "team-0",
      "weight": 1
    },
    {
      "exposure": 4,
      "id": "aggregate-reversal-0-4",
      "outcome": 4,
      "stratum_id": "team-0",
      "weight": 2
    },
    {
      "exposure": 5,
      "id": "aggregate-reversal-0-5",
      "outcome": 5,
      "stratum_id": "team-0",
      "weight": 3

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "aggregate_relationship": {
    "confidence_interval": [
      -0.9623,
      -0.8639
    ],
    "raw_slope": -1.842,
    "standardized_slope": -0.9398
  },
  "aggregation_use": "aggregate_relationship_not_decision_safe",
  "decision": "aggregate_within_stratum_reversal_detected",
  "interpretation": "A reversal means an executive rollup answers a different question from comparable within-stratum changes; it does not by itself identify a causal mechanism.",
  "method": "weighted_fixed_effect_simpsons_reversal_cluster_bootstrap_v1",
  "reversal": {
    "bootstrap_probability": 1,
    "minimum_absolute_standardized_slope": 0.1,
    "minimum_probability": 0.9,
    "observed": true
  },
  "sample": {
    "bootstrap_draws": 200,
    "rows": 80,
    "strata": 8,
    "total_weight": 152
  },
  "strata": [
    {
      "exposure_mean": 4.5789,
      "outcome_mean": 4.5789,
      "raw_slope": 1,
      "rows": 10,
      "standardized_slope": 1,
      "stratum_id": "team-0",
      "weight": 19
    },
    {
      "exposure_mean": 9.5789,
      "outcome_mean": -5.4211,
      "raw_slope": 1,
      "rows": 10,
      "standardized_slope": 1,
      "stratum_id": "team-1",
      "weight": 19
    },

How it works

Statistical audit & measurement — Check whether a number is fit to decide on: coverage, timing, reconciliation, and the gaps a dashboard hides.

1. 1 Fit the population-weighted aggregate exposure/outcome slope at the declared observation grain and standardize it for comparability.
2. 2 Remove weighted stratum means from exposure and outcome, then fit the weighted fixed-effect slope using only within-stratum variation.
3. 3 Resample entire strata and then rows within each sampled stratum so uncertainty preserves the clustered data-generating structure.
4. 4 Flag a reversal only when the slopes have opposite signs, both clear the practical standardized-magnitude threshold, and the bootstrap reversal probability clears its governed threshold.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "detect simpsonsparadoxstyle sign reversals between an" }
  → finds "audit_aggregate_metric_reversal"

gitrevio_capability_describe
  { "capability_id": "audit_aggregate_metric_reversal" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "audit_aggregate_metric_reversal", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

Related tools

See every tool in Measurement integrity →