Estimate staggered policy rollout effects

Estimate cohort-aware dynamic effects of a team-by-team policy rollout against not-yet-treated controls, with a simultaneous pretrend identification gate.

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

What it's for

Turns real phased rollouts of review rules, AI tools, team topology, or reliability practices into defensible effect curves instead of before/after anecdotes.

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
bootstrap_draws integer ≥ 200, ≤ 20000 Numerical control Optional
confidence_level number ≥ 0.8, ≤ 0.999 Your calibration Optional
equivalence_margin number > 0 Your calibration Optional
max_detail_rows integer ≥ 1, ≤ 500 Numerical control Optional
minimum_cohort_units integer ≥ 2, ≤ 10000 Your calibration Optional
minimum_control_units integer ≥ 2, ≤ 100000 Your calibration Optional
minimum_practical_effect number ≥ 0 Your calibration Optional
observations array of objects (5 fields) ≥ 240 items Evidence Yes
post_periods integer ≥ 1, ≤ 50 Your calibration Optional
pre_periods integer ≥ 2, ≤ 20 Your calibration Optional
seed integer Numerical control Optional

Each observations record

Field Type Required
adoption_period integer,null Yes
id string (non-empty) Yes
outcome number Yes
period integer Yes
unit_id string (non-empty) Yes
Example input 
{
  "bootstrap_draws": 200,
  "equivalence_margin": 0.2,
  "minimum_practical_effect": 0.5,
  "observations": [
    {
      "adoption_period": 5,
      "id": "rollout-example-0-0",
      "outcome": 0,
      "period": 0,
      "unit_id": "team-0"
    },
    {
      "adoption_period": 5,
      "id": "rollout-example-0-1",
      "outcome": 0.2,
      "period": 1,
      "unit_id": "team-0"
    },
    {
      "adoption_period": 5,
      "id": "rollout-example-0-2",
      "outcome": 0.4,
      "period": 2,
      "unit_id": "team-0"
    },
    {
      "adoption_period": 5,
      "id": "rollout-example-0-3",
      "outcome": 0.6000000000000001,
      "period": 3,
      "unit_id": "team-0"
    },
    {
      "adoption_period": 5,
      "id": "rollout-example-0-4",
      "outcome": 0.8,
      "period": 4,
      "unit_id": "team-0"
    },
    {
      "adoption_period": 5,
      "id": "rollout-example-0-5",
      "outcome": 2.5,

Truncated for display — the full payload is 3368 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": [
    "Treatment adoption is absorbing and the event-study compares each cohort with units not yet treated at the corresponding calendar time.",
    "No anticipation, consistency, limited interference, common outcome measurement, and cohort-specific parallel trends are required for a causal interpretation.",
    "The overall effect is a treated-observation-weighted average across supported post-adoption event times, not an individual employee score.",
    "A causal rollout claim is withheld unless simultaneous pre-treatment intervals fit inside the governed equivalence margin."
  ],
  "decision": "rollout_effect_robustly_positive",
  "detail": {
    "returned_rows": 10,
    "total_rows": 10,
    "truncated": false
  },
  "event_study": [
    {
      "effect": 0,
      "eligible_cohorts": 2,
      "event_time": -4,
      "interval": {
        "high": 0,
        "low": 0
      },
      "minimum_control_units": 20,
      "treated_units": 20
    },
    {
      "effect": 0,
      "eligible_cohorts": 2,
      "event_time": -3,
      "interval": {
        "high": 0,
        "low": 0
      },
      "minimum_control_units": 20,
      "treated_units": 20
    },
    {
      "effect": 0,
      "eligible_cohorts": 2,
      "event_time": -2,
      "interval": {
        "high": 0,
        "low": 0
      },

Truncated for display — the full payload is 189 lines.

How it works

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

  1. 1 Estimate cohort-aware dynamic effects of a team-by-team policy rollout against not-yet-treated controls, with a simultaneous pretrend identification gate.
  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

  • observations: at least 240 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

  • balanced consecutive team-period outcome panel
  • first deployment period per team for one immutable policy definition
  • not-yet-treated risk sets at each cohort/event time
  • policy and outcome definition
  • analysis epoch, causal exclusion rules, and equivalence margin
  • minimum practical effect and cohort/control support

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": "estimate cohortaware dynamic effects of a" }
  → finds "estimate_staggered_policy_rollout_effects"

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

gitrevio_capability_run
  { "capability_id": "estimate_staggered_policy_rollout_effects", "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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