Estimate transportable intervention effect

Transport intervention effects to a target environment with similarity-weighted random-effects meta-regression, support diagnostics, and leave-one-environment-out validation.

Statistical audit & measurement Estimate CTOs Tech CEOs IT managers Team leads Investors

What it's for

Answers whether evidence from prior teams or portfolio companies plausibly generalizes to a new context before paying for a target trial.

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
minimum_effective_sources number ≥ 1 Your calibration Optional
minimum_practical_effect number Your calibration Optional
moderator_names array of string ≥ 1 item Evidence Yes
ridge number ≥ 0, ≤ 100 Your calibration Optional
similarity_bandwidth number ≥ 0.1, ≤ 10 Your calibration Optional
source_environments array of objects (4 fields) Evidence Yes
target_moderators object Evidence Yes

Each source_environments record

Field Type Required
effect_estimate number Yes
id string (non-empty) Yes
moderators object Yes
standard_error number (> 0) Yes
Example input 
{
  "minimum_practical_effect": 0.5,
  "moderator_names": [
    "maturity"
  ],
  "ridge": 0.0001,
  "similarity_bandwidth": 2,
  "source_environments": [
    {
      "effect_estimate": 0,
      "id": "environment-0",
      "moderators": {
        "maturity": -2
      },
      "standard_error": 0.1
    },
    {
      "effect_estimate": 0.5,
      "id": "environment-1",
      "moderators": {
        "maturity": -1
      },
      "standard_error": 0.1
    },
    {
      "effect_estimate": 1,
      "id": "environment-2",
      "moderators": {
        "maturity": 0
      },
      "standard_error": 0.1
    },
    {
      "effect_estimate": 1.5,
      "id": "environment-3",
      "moderators": {
        "maturity": 1
      },
      "standard_error": 0.1
    },
    {
      "effect_estimate": 2,
      "id": "environment-4",
      "moderators": {

Truncated for display — the full payload is 53 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": [
    "Each source effect is causally identified for its source environment and reported with a valid standard error.",
    "The supplied moderators include the material effect modifiers needed for conditional transportability.",
    "Source environments are independent; similarity weighting discounts distance but cannot repair omitted target-support gaps.",
    "Eligibility means evidence supports a monitored target trial, not automatic organization-wide rollout."
  ],
  "decision": "eligible_for_target_trial",
  "heterogeneity": {
    "degrees_freedom": 3,
    "q_statistic": 0,
    "tau_squared": 0
  },
  "leave_one_environment_out": {
    "interval_coverage": 1,
    "rmse": 0
  },
  "method": "similarity_weighted_random_effects_transport_v1",
  "source_influence": [
    {
      "environment_id": "environment-3",
      "normalized_weight": 0.2366,
      "standardized_distance": 0
    },
    {
      "environment_id": "environment-2",
      "normalized_weight": 0.2222,
      "standardized_distance": 0.7071
    },
    {
      "environment_id": "environment-4",
      "normalized_weight": 0.2222,
      "standardized_distance": 0.7071
    },
    {
      "environment_id": "environment-1",
      "normalized_weight": 0.1842,
      "standardized_distance": 1.4142
    },
    {
      "environment_id": "environment-0",
      "normalized_weight": 0.1348,
      "standardized_distance": 2.1213
    }

Truncated for display — the full payload is 62 lines.

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 Transport intervention effects to a target environment with similarity-weighted random-effects meta-regression, support diagnostics, and leave-one-environment-out validation.
  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

  • Metric definitions, weights, aggregate grain, sampling, missingness, dependence, and comparison windows correspond to the management claim being audited.
  • Association, instability, or measurement quality is not a causal effect and must not be converted directly into an individual employment decision.

Minimum evidence

  • source_environments: required and organization-defined
  • moderator_names: at least 1 rows/items
  • target_moderators: required and organization-defined

How to validate it

Validate on future periods or held-out aggregate units, compare with a simple baseline, and require stability across plausible metric definitions and decision thresholds.

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

  • metric/outcome definitions, entity grain, observation window, costs, thresholds, priors, and constraints represented by the function inputs

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": "transport intervention effects to a target" }
  → finds "estimate_transportable_intervention_effect"

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

gitrevio_capability_run
  { "capability_id": "estimate_transportable_intervention_effect", "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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