Audit causal claim negative controls

What it's for

Adds an explicit placebo boundary around causal attribution so a persuasive target estimate cannot hide failures elsewhere in the same design or data pipeline.

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.

{
  "minimum_practical_effect": 0.5,
  "negative_controls": [
    {
      "effect_estimate": 0,
      "id": "placebo-0",
      "kind": "outcome",
      "standard_error": 0.2
    },
    {
      "effect_estimate": 0,
      "id": "placebo-1",
      "kind": "exposure",
      "standard_error": 0.2
    },
    {
      "effect_estimate": 0,
      "id": "placebo-2",
      "kind": "outcome",
      "standard_error": 0.2
    },
    {
      "effect_estimate": 0,
      "id": "placebo-3",
      "kind": "exposure",
      "standard_error": 0.2
    }
  ],
  "target_effect_estimate": 1,
  "target_standard_error": 0.1
}

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": [
    "Controls were prespecified because the treatment should not causally affect them, not selected after viewing estimates.",
    "Control estimates and standard errors are valid under the same design and data pipeline as the target estimate.",
    "Control z-scores are approximately independent for the omnibus chi-square reference; materially correlated controls require prespecified prewhitening or a joint covariance model.",
    "Passing controls rules out detected violations only; it does not prove exchangeability, consistency, positivity, or correct measurement.",
    "A failed control pauses the causal claim and triggers design or data investigation rather than automatic effect correction."
  ],
  "controls": [
    {
      "control_id": "placebo-0",
      "effect_estimate": 0,
      "expected_effect": 0,
      "falsification_failure": false,
      "kind": "outcome",
      "p_value": 1,
      "q_value": 1,
      "standard_error": 0.2,
      "z_score": 0
    },
    {
      "control_id": "placebo-1",
      "effect_estimate": 0,
      "expected_effect": 0,
      "falsification_failure": false,
      "kind": "exposure",
      "p_value": 1,
      "q_value": 1,
      "standard_error": 0.2,
      "z_score": 0
    },
    {
      "control_id": "placebo-2",
      "effect_estimate": 0,
      "expected_effect": 0,
      "falsification_failure": false,
      "kind": "outcome",
      "p_value": 1,
      "q_value": 1,
      "standard_error": 0.2,
      "z_score": 0
    },
    {
      "control_id": "placebo-3",

How it works

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

1. 1 Gate a causal effect claim using prespecified negative outcome/exposure controls, Benjamini-Hochberg multiplicity control, and an omnibus chi-square falsification test.
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": "gate a causal effect claim using" }
  → finds "audit_causal_claim_negative_controls"

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

gitrevio_capability_run
  { "capability_id": "audit_causal_claim_negative_controls", "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 Causal evidence & experiments →