Monitor sequential intervention experiment

Monitor cumulative binary intervention outcomes with beta-binomial posteriors, Bayes factors, expected regret, and preregistered success, harm, or futility stopping.

Sequential Bayesian & bandits Audit CTOs Tech CEOs IT managers Team leads

What it's for

Lets you look at an experiment as it runs without inflating false positives, with success, harm and futility stopping rules fixed in advance.

Closes the intervention loop with an anytime-readable experiment decision path instead of repeated-peeking p-values.

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
bayes_factor_threshold number ≥ 1 Your calibration Optional
checkpoints array of objects (5 fields) Evidence Yes
futility_probability number ≥ 0, ≤ 1 Your calibration Optional
harm_probability number ≥ 0, ≤ 1 Your calibration Optional
minimum_effect number Your calibration Optional
minimum_trials_per_arm integer ≥ 1 Your calibration Optional
posterior_draws integer ≥ 500, ≤ 100000 Numerical control Optional
prior_alpha number > 0 Your calibration Optional
prior_beta number > 0 Your calibration Optional
seed integer Numerical control Optional
success_probability number ≥ 0, ≤ 1 Your calibration Optional

Each checkpoints record

Field Type Required
control_successes integer (≥ 0) Yes
control_trials integer (≥ 1) Yes
id string (non-empty) Yes
treatment_successes integer (≥ 0) Yes
treatment_trials integer (≥ 1) Yes
Example input 
{
  "bayes_factor_threshold": 3,
  "checkpoints": [
    {
      "control_successes": 24,
      "control_trials": 50,
      "id": "week-1",
      "treatment_successes": 30,
      "treatment_trials": 50
    },
    {
      "control_successes": 49,
      "control_trials": 100,
      "id": "week-2",
      "treatment_successes": 70,
      "treatment_trials": 100
    }
  ],
  "minimum_effect": 0.05,
  "minimum_trials_per_arm": 50,
  "posterior_draws": 1000,
  "seed": 4
}

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 
{
  "audit_path": [
    {
      "after_stopping_boundary": false,
      "bayes_factor_10": 0.498,
      "checkpoint_id": "week-1",
      "control_trials": 50,
      "decision": "continue",
      "expected_regret_of_posterior_best_arm": 0.0037,
      "lift": {
        "mean": 0.1191,
        "p05": -0.0287,
        "p50": 0.1185,
        "p95": 0.2621
      },
      "log_bayes_factor_10": -0.6971,
      "posterior_control_rate": 0.4808,
      "posterior_treatment_rate": 0.5962,
      "probability_harm": 0.091,
      "probability_lift_exceeds_minimum": 0.764,
      "treatment_trials": 50
    },
    {
      "after_stopping_boundary": false,
      "bayes_factor_10": 16.551,
      "checkpoint_id": "week-2",
      "control_trials": 100,
      "decision": "stop_success",
      "expected_regret_of_posterior_best_arm": 0,
      "lift": {
        "mean": 0.2091,
        "p05": 0.0965,
        "p50": 0.2108,
        "p95": 0.3192
      },
      "log_bayes_factor_10": 2.8064,
      "posterior_control_rate": 0.4902,
      "posterior_treatment_rate": 0.6961,
      "probability_harm": 0.001,
      "probability_lift_exceeds_minimum": 0.99,
      "treatment_trials": 100
    }
  ],
  "decision": "stop_success",

Truncated for display — the full payload is 61 lines.

How it works

Sequential Bayesian & bandits — Learn while deciding — update beliefs as evidence arrives and choose where the next unit of effort is worth spending.

  1. 1 Monitor cumulative binary intervention outcomes with beta-binomial posteriors, Bayes factors, expected regret, and preregistered success, harm, or futility stopping.
  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

  • The likelihood or reward model, prior support, action logging, delayed outcomes, and any stationarity assumptions match the deployment process.
  • Posterior probability and adaptive selection are model-conditional; they are not substitutes for randomized propensities or guaranteed safety.

Minimum evidence

  • checkpoints: 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. 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": "monitor cumulative binary intervention outcomes with" }
  → finds "monitor_sequential_intervention_experiment"

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

gitrevio_capability_run
  { "capability_id": "monitor_sequential_intervention_experiment", "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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