Estimate feature incremental value

What it's for

Translates feature experiment outcomes into a posterior distribution of incremental financial value rather than celebrating uplift without economics.

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_probability_positive_value": 0.8,
  "posterior_draws": 5000,
  "rollout_cost": 75000,
  "seed": 17,
  "segments": [
    {
      "contribution_value_per_conversion": 240,
      "control_successes": 120,
      "control_trials": 1000,
      "eligible_accounts": 8000,
      "id": "small-business",
      "treated_successes": 160,
      "treated_trials": 1000
    },
    {
      "contribution_value_per_conversion": 900,
      "control_successes": 84,
      "control_trials": 600,
      "eligible_accounts": 2000,
      "id": "mid-market",
      "treated_successes": 105,
      "treated_trials": 600
    }
  ]
}

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": [
    "Treatment/control counts come from a valid randomized or conditionally exchangeable design with stable exposure, no material interference, complete outcome maturity, and consistent segment membership.",
    "Eligible accounts, contribution per conversion, and rollout cost are incremental finance-owned quantities over one horizon without cross-segment double counting.",
    "Beta-binomial uncertainty covers conversion sampling only; adoption, duration, cannibalization, treatment heterogeneity beyond segments, and causal-design error require additional modeling."
  ],
  "configuration": {
    "beta_prior_alpha": 1,
    "beta_prior_beta": 1,
    "confidence_level": 0.9,
    "minimum_probability_positive_value": 0.8,
    "posterior_draws": 5000,
    "seed": 17
  },
  "decision": "feature_incremental_value_supported",
  "method": "beta_binomial_segment_feature_incremental_value_v1",
  "segment_diagnostics": [
    {
      "expected_incremental_value_before_rollout_cost": 77066.5402,
      "posterior_mean_uplift": 0.0401,
      "probability_positive_segment_value": 0.9934,
      "probability_positive_uplift": 0.9934,
      "segment_id": "small-business",
      "uplift_interval": [
        0.0152,
        0.0658
      ]
    },
    {
      "expected_incremental_value_before_rollout_cost": 62968.7665,
      "posterior_mean_uplift": 0.035,
      "probability_positive_segment_value": 0.946,
      "probability_positive_uplift": 0.946,
      "segment_id": "mid-market",
      "uplift_interval": [
        -0.0008,
        0.069
      ]
    }
  ],
  "summary": {
    "expected_incremental_value": 65035.3067,
    "incremental_value_interval": [
      -14302.1922,

How it works

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

1. 1 Freeze randomized or conditionally exchangeable treated/control cohorts, mature conversion outcomes, non-overlapping segments, eligible future accounts, contribution per conversion, and rollout cost.
2. 2 Draw treated and control conversion posteriors per segment, propagate uplift through eligible accounts and contribution, aggregate segments, subtract rollout cost, and report the value interval and probability positive.
3. 3 Approve value only after the governed probability gate and separate checks for assignment integrity, interference, attrition, exposure maturity, cannibalization, and transport to the rollout population.

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 rollout value from segmentlevel treatedcontrol" }
  → finds "estimate_feature_incremental_value"

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

gitrevio_capability_run
  { "capability_id": "estimate_feature_incremental_value", "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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