Estimate role adjusted contribution

Estimate role-relative outcome contributions with empirical-Bayes shrinkage, uncertainty, provenance, and cohort privacy.

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

What it's for

Makes contribution variation inspectable without reducing anyone to a commit count — role-relative, shrunk toward the mean, with uncertainty and cohort privacy preserved.

Makes contribution variation inspectable without reducing people to raw activity counts or automatic employment labels.

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.

Field	Type	Role	Required
contributors	array of objects (3 fields)	Evidence	Yes
min_observations	integer ≥ 2	Your calibration	Optional
min_role_cohort	integer ≥ 3	Your calibration	Optional
prior_variance_floor	number > 0	Your calibration	Optional

Each `contributors` record

Field	Type	Required
id	string (non-empty)	Yes
observations	array of objects (3 fields)	Yes
role	string (non-empty)	Yes

Example input

{
  "contributors": [
    {
      "id": "ada",
      "observations": [
        {
          "effect": 0.75,
          "evidence_id": "ada-0",
          "standard_error": 0.15
        },
        {
          "effect": 0.8,
          "evidence_id": "ada-1",
          "standard_error": 0.15
        },
        {
          "effect": 0.8500000000000001,
          "evidence_id": "ada-2",
          "standard_error": 0.15
        }
      ],
      "role": "backend_engineer"
    },
    {
      "id": "bea",
      "observations": [
        {
          "effect": 0.35000000000000003,
          "evidence_id": "bea-0",
          "standard_error": 0.15
        },
        {
          "effect": 0.4,
          "evidence_id": "bea-1",
          "standard_error": 0.15
        },
        {
          "effect": 0.45,
          "evidence_id": "bea-2",
          "standard_error": 0.15
        }
      ],
      "role": "backend_engineer"
    },

Truncated for display — the full payload is 109 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

{
  "estimates": [
    {
      "credible_interval_95": {
        "high": 0.9513,
        "low": 0.6155
      },
      "evidence_ids": [
        "ada-0",
        "ada-1",
        "ada-2"
      ],
      "id": "ada",
      "interpretation": "credible_above_role_benchmark",
      "observation_count": 3,
      "posterior_effect": 0.7834,
      "probability_above_role_benchmark": 1,
      "role": "backend_engineer",
      "role_rank": 1
    },
    {
      "credible_interval_95": {
        "high": 0.56,
        "low": 0.2243
      },
      "evidence_ids": [
        "bea-0",
        "bea-1",
        "bea-2"
      ],
      "id": "bea",
      "interpretation": "credible_above_role_benchmark",
      "observation_count": 3,
      "posterior_effect": 0.3921,
      "probability_above_role_benchmark": 1,
      "role": "backend_engineer",
      "role_rank": 2
    },
    {
      "credible_interval_95": {
        "high": 0.1687,
        "low": -0.167
      },
      "evidence_ids": [

Truncated for display — the full payload is 110 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 Estimate role-relative outcome contributions with empirical-Bayes shrinkage, uncertainty, provenance, and cohort privacy.
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

contributors: 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 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
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 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
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 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
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 rolerelative outcome contributions with empiricalbayes" }
  → finds "estimate_role_adjusted_contribution"

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

gitrevio_capability_run
  { "capability_id": "estimate_role_adjusted_contribution", "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

Bayesian account risk triage

Prioritize human review of auditable account-security and policy-conflict evidence using Bayes factors and decision costs.

Sequential Bayesian & bandits

Analyze info gap robust satisficing

Select a robust-satisficing action under severe uncertainty with Info-Gap Decision Theory: evaluate worst and best payoff across a governed nested uncertainty envelope, maximize the radius before a critical requirement fails, report windfall opportuneness, and use no scenario probabilities.

Decision analysis

Audit attention fragmentation evidence integrity

Audit consented point-in-time contributor identity, availability, privacy-safe calendar metadata and work-session lineage before reporting aggregate meeting load, protected focus blocks or cross-project switching.

Statistical audit & measurement

Audit attrition risk prediction integrity

Audit an attrition model's complete eligible cohort, point-in-time features, supportive-use governance, intervention-contaminated labels, competing outcomes, calibration, false positives and authorized subgroup error before any person-level use.

Forecasting & survival

Audit code knowledge concentration integrity

Audit file, module, service, or repository knowledge concentration from point-in-time substantive changes, reviews, incident response and documentation using identity-confidence filtering, recency decay, Bayesian ownership uncertainty, entropy-effective owners, HHI and leave-top-owner-out resilience—without turning contribution evidence into a person-performance score.