Audit cluster randomization integrity

Audit cluster-randomized experiments for practical baseline imbalance and differential outcome observation, with cluster-size-weighted standardized differences and assignment permutation diagnostics.

Causal inference & experiment design Audit CTOs Tech CEOs IT managers Team leads Investors

What it's for

Gives agents and leaders a pre-analysis integrity gate before a randomized team rollout is described as credible evidence.

What you give it

Every tool is a pure function: it computes from what you pass it and reads nothing on its own. Inputs split into evidence you supply from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

Field Type Role Required
clusters array of objects (5 fields) ≥ 40 items Evidence Yes
max_detail_rows integer ≥ 1, ≤ 500 Numerical control Optional
maximum_observation_rate_difference number ≥ 0, ≤ 1 Your calibration Optional
maximum_standardized_imbalance number ≥ 0.01, ≤ 2 Your calibration Optional
metric_names array of string ≥ 1 item Evidence Yes
permutation_draws integer ≥ 200, ≤ 20000 Numerical control Optional
seed integer Numerical control Optional

Each clusters record

Field Type Required
assigned_arm integer (≥ 0, ≤ 1) Yes
baseline_metrics object Yes
cluster_size number (≥ 1) Optional
id string (non-empty) Yes
outcome_observed boolean Optional
Example input 
{
  "clusters": [
    {
      "assigned_arm": 0,
      "baseline_metrics": {
        "cycle_time": 0,
        "failure_rate": 0,
        "throughput": 0
      },
      "cluster_size": 8,
      "id": "experiment-cluster-0",
      "outcome_observed": false
    },
    {
      "assigned_arm": 1,
      "baseline_metrics": {
        "cycle_time": 1.85,
        "failure_rate": 0.17525773195876287,
        "throughput": 2.9
      },
      "cluster_size": 9,
      "id": "experiment-cluster-1",
      "outcome_observed": true
    },
    {
      "assigned_arm": 0,
      "baseline_metrics": {
        "cycle_time": 3.7,
        "failure_rate": 0.35051546391752575,
        "throughput": 5.8
      },
      "cluster_size": 10,
      "id": "experiment-cluster-2",
      "outcome_observed": true
    },
    {
      "assigned_arm": 1,
      "baseline_metrics": {
        "cycle_time": 0.5,
        "failure_rate": 0.5257731958762887,
        "throughput": 8.7
      },
      "cluster_size": 11,
      "id": "experiment-cluster-3",

Truncated for display — the full payload is 452 lines.

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.

Example output 
{
  "assumptions": [
    "Assignment was generated at cluster grain before outcomes, and baseline metrics were frozen before assignment was revealed.",
    "Practical standardized-difference and observation-rate limits are governed tolerances; randomization p-values are diagnostics, not substitutes for those limits.",
    "Cluster-size weighting targets the represented population; an equal-cluster estimand requires equal weights instead.",
    "Passing balance and observation checks does not rule out interference, noncompliance, outcome-definition changes, or post-randomization exclusions."
  ],
  "baseline_balance": {
    "mahalanobis_distance": 0.0297,
    "maximum_absolute_standardized_difference": 0.1336,
    "maximum_allowed": 0.5,
    "practical_balance_supported": true,
    "randomization_p_value_mahalanobis": 0.950249,
    "randomization_p_value_max_imbalance": 0.955224
  },
  "decision": "cluster_randomization_integrity_supported",
  "detail": {
    "returned_rows": 3,
    "total_rows": 3,
    "truncated": false
  },
  "method": "cluster_randomization_balance_attrition_permutation_audit_v1",
  "metrics": [
    {
      "absolute_standardized_difference": 0.1336,
      "metric": "cycle_time",
      "standardized_difference": -0.1336,
      "within_limit": true
    },
    {
      "absolute_standardized_difference": 0.1077,
      "metric": "throughput",
      "standardized_difference": 0.1077,
      "within_limit": true
    },
    {
      "absolute_standardized_difference": 0.0355,
      "metric": "failure_rate",
      "standardized_difference": 0.0355,
      "within_limit": true
    }
  ],
  "outcome_observation": {
    "control_rate": 0.96,

Truncated for display — the full payload is 59 lines.

How it works

Causal inference & experiment design — Separate what a change caused from what merely moved alongside it.

  1. 1 Audit cluster-randomized experiments for practical baseline imbalance and differential outcome observation, with cluster-size-weighted standardized differences and assignment permutation diagnostics.
  2. 2 Evaluate the method-specific diagnostics and gates returned by the function, then abstain unless the declared decision clears them under locally governed thresholds.

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.

Assumptions & guardrails

  • Assignment or adoption timing, interference policy, overlap, attrition, and outcome availability match the estimand encoded by the method.
  • A causal label is warranted only when design and diagnostic requirements pass; otherwise treat the result as descriptive or abstain.

Minimum evidence

  • clusters: at least 40 rows/items
  • metric_names: at least 1 rows/items

How to validate it

Preserve the assignment/adoption design and validate overlap, pre-period or placebo diagnostics, attrition, interference policy, and cluster-level uncertainty; never tune on the estimated effect.

Calibrating it to your org

Same for everyone

The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.

Specific to you

  • one frozen row per randomized cluster
  • cluster-size weight at the declared estimand date
  • baseline metric family
  • practical standardized-imbalance limit
  • maximum differential observation-rate limit

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 20 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": "audit clusterrandomized experiments for practical baseline" }
  → finds "audit_cluster_randomization_integrity"

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

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

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

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