Estimate productivity rebound effect

What it's for

Reveals whether promised AI or process productivity becomes actual capacity savings, valuable extra output, or hidden workload backfill—and quantifies the distinction causally.

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.

{
  "bootstrap_draws": 200,
  "maximum_acceptable_rebound_fraction": 0.8,
  "maximum_follow_up_period": 1,
  "observations": [
    {
      "comparison_set_id": "match-0",
      "id": "rebound-0-control--2",
      "output_units": 100,
      "relative_period": -2,
      "resource_input_units": 100,
      "treated_group": false,
      "unit_id": "match-0-control",
      "weight": 1
    },
    {
      "comparison_set_id": "match-0",
      "id": "rebound-0-control--1",
      "output_units": 100,
      "relative_period": -1,
      "resource_input_units": 100,
      "treated_group": false,
      "unit_id": "match-0-control",
      "weight": 1
    },
    {
      "comparison_set_id": "match-0",
      "id": "rebound-0-control-0",
      "output_units": 100,
      "relative_period": 0,
      "resource_input_units": 100,
      "treated_group": false,
      "unit_id": "match-0-control",
      "weight": 1
    },
    {
      "comparison_set_id": "match-0",
      "id": "rebound-0-control-1",
      "output_units": 100,
      "relative_period": 1,
      "resource_input_units": 100,
      "treated_group": false,
      "unit_id": "match-0-control",
      "weight": 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.

{
  "configuration": {
    "bootstrap_draws": 200,
    "confidence_level": 0.9,
    "maximum_follow_up_period": 1,
    "seed": 7
  },
  "decision": "productivity_rebound_supported_within_governed_limit",
  "excluded_comparison_sets": [],
  "failed_gates": [],
  "guardrails": [
    "Rebound is estimated for aggregate output and resource units inside matched comparison sets; it is not an individual productivity score and must not be used for hiring, firing, surveillance or workload intensification.",
    "The stacked design requires a credible untreated comparison group, a clean reference period, parallel pretrends, stable measurement and no spillovers or concurrent differential intervention; failing those gates means abstention, not zero rebound.",
    "Efficiency at fixed output, induced output and total resource use answer different questions: positive productivity can coexist with high rebound or backfire when newly available capacity creates valuable additional demand.",
    "A rebound above 100% can be economically rational if induced output is valuable; pair this estimate with incremental value, quality, wellbeing, emissions and capacity constraints before changing policy."
  ],
  "method": "stacked_matched_cohort_log_did_rebound_bootstrap_v1",
  "period_effects": [
    {
      "comparison_set_count": 2,
      "output_log_effect": 0,
      "relative_period": -2,
      "resource_input_log_effect": 0,
      "resource_intensity_log_effect": 0
    },
    {
      "comparison_set_count": 2,
      "output_log_effect": 0.0953,
      "relative_period": 0,
      "resource_input_log_effect": -0.1278,
      "resource_intensity_log_effect": -0.2231
    },
    {
      "comparison_set_count": 2,
      "output_log_effect": 0.0953,
      "relative_period": 1,
      "resource_input_log_effect": -0.1278,
      "resource_intensity_log_effect": -0.2231
    }
  ],
  "summary": {
    "aggregate_unit_count": 4,
    "efficiency_gain_confidence_interval": [
      0.2,

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 Construct prospectively matched treated/control comparison sets at aggregate-unit grain with a clean relative-period minus-one reference, stable output and resource definitions, weights and at least two comparison sets.
2. 2 Estimate within-set treated-minus-control log changes for output, total resource input and resource intensity by event period; audit pre-intervention trends and average eligible follow-up effects without mixing calendar adoption cohorts.
3. 3 Translate intensity into fixed-output efficiency, output into induced demand, and total input into realized resource change; calculate rebound as the fraction of expected fixed-output saving reabsorbed and cluster-bootstrap its interval.

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 how much aggregate capacity released" }
  → finds "estimate_productivity_rebound_effect"

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

gitrevio_capability_run
  { "capability_id": "estimate_productivity_rebound_effect", "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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