Estimate synthetic control impact

What it's for

When you can't randomize, this builds a weighted counterfactual from comparable units and tests it against placebos, so an effect claim survives more than a before-and-after chart.

Fills the repository's documented synthetic-control gap and tests whether management changes caused measurable outcomes.

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.

{
  "donor_series": [
    {
      "id": "control-a",
      "values": [
        10,
        10.2,
        10.4,
        10.6,
        10.8,
        11,
        11.2,
        11.4,
        11.6,
        11.8,
        12,
        12.2
      ]
    },
    {
      "id": "control-b",
      "values": [
        9,
        9.35,
        9.5,
        9.85,
        10,
        10.35,
        10.5,
        10.85,
        11,
        11.35,
        11.5,
        11.85
      ]
    },
    {
      "id": "control-c",
      "values": [
        11,
        11.05,
        11.100000000000001,
        11.45,
        11.5,

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.

{
  "assumptions": [
    "No other treated-unit-specific shock occurred at the intervention time.",
    "Donor outcomes were not affected by the intervention and their pre-period relationship would have continued.",
    "Placebo intervals and p-values are empirical diagnostics, not proof of random assignment."
  ],
  "counterfactual_model": {
    "fit_quality": "strong",
    "intercept": 0.015,
    "normalized_prefit_error": 0.028,
    "optimization_iterations": 4925,
    "pre_rmspe": 0.0129,
    "weights": [
      {
        "donor_id": "control-a",
        "weight": 0.6975
      },
      {
        "donor_id": "control-b",
        "weight": 0.1574
      },
      {
        "donor_id": "control-c",
        "weight": 0.1451
      }
    ]
  },
  "impact": {
    "average_post_effect": 2.0018,
    "cumulative_post_effect": 8.007,
    "empirical_placebo_p_value": 0.25,
    "post_pre_rmspe_ratio": 155.7637
  },
  "leave_one_donor_out_average_effect": {
    "max": 2.0146,
    "min": 1.9851
  },
  "method": "constrained_synthetic_control_placebo_v1",
  "period_effects": [
    {
      "counterfactual": 11.5786,
      "effect": 2.0214,
      "observed": 13.6,
      "period": "week-08",

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 Estimate intervention effects against a constrained donor-weighted counterfactual with placebo inference and donor sensitivity.
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.

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": "estimate intervention effects against a constrained" }
  → finds "estimate_synthetic_control_impact"

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

gitrevio_capability_run
  { "capability_id": "estimate_synthetic_control_impact", "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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