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Experiment Validity Auditor

by Agentlas

Audits an A/B test result by first testing the realized split for sample ratio mismatch, then recomputing the effect with always-valid bounds when the dashboard was peeked at, controlling multiplicity across every hypothesis examined, and applying pre-assignment covariate variance reduction.

Example conversation

Try asking like this

You

we peeked at the dashboard daily and stopped when it hit significance, is the 6% real

Experiment Validity Auditor

A validity-first audit method for online controlled experiments, built on two invariants: the assignment is audited before the effect, because a split that arrived unbalanced means the arms stopped being exchangeable and the cause of the imbalance is now inside the measured difference; and the analysis must match the procedure actually used, because a fixed-horizon p-value is not the error rate of a dashboard that was checked daily and stopped the moment it crossed. It reconstructs the experiment card marking undeclared fields as such, runs a strict goodness-of-fit sample-ratio-mismatch test globally and per segment and stops the effect analysis when it fires while diagnosing the cause from its signature, reconciles randomization unit against analysis unit so clustered observations are not treated as independent, quantifies crossover and trigger dilution and names the estimand, recomputes the effect with an always-valid or group-sequential bound printed beside the naive number, controls multiplicity against every hypothesis examined rather than every one reported, applies covariate adjustment restricted to strictly pre-assignment variables, bounds guardrail harm rather than accepting a null, separates novelty from steady state, assesses interference, and returns a three-part verdict of validity, interval, and what would make the result conclusive without ever recommending whether to ship.

What I need first
  • Raw per-unit assignment records at the randomization unit with timestamps and arm. Realized counts cannot be tested for ratio mismatch from an aggregate, and this is the gate the whole audit rests on.
  • Raw exposure and outcome events joinable to the assignment records, so every figure is recomputed rather than re-reported from the dashboard under audit.
  • Hypothesis, primary metric, randomization and analysis unit, planned sample size with its power calculation, planned duration, and stopping rule - or the explicit statement that none was declared, which is itself a finding.
  • How many times results were viewed and whether looking influenced when the test stopped, corroborated where possible. This decides whether a fixed-horizon analysis is applicable at all.
  • Exact computation of each metric including every filter and, critically, whether each filter is applied before or after assignment. A post-assignment filter is a bias, not a cleanup step.
  • Metric values measured strictly before assignment, used for variance reduction and for the pre-period equivalence check. Post-assignment variables are excluded because adjusting on them reintroduces bias.Optional
  • Guardrail metrics with their harm tolerances, so safety can be reported as a bound rather than as an underpowered null.Optional
  • Segments examined during the experiment. They enter the multiplicity denominator whether or not they were reported.Optional
  • Releases, incidents, holidays, and marketing pushes inside the window that could produce or mask an effect independently of the treatment.Optional
What you get
  • Experiment card with undeclared fields marked, the ratio-mismatch test with observed and expected counts and per-segment results, the diagnosis where it fired, unit reconciliation with crossover and trigger rates, and the pre-period equivalence check.
  • The effect recomputed for the procedure actually used, with the always-valid or group-sequential interval printed beside the naive fixed-horizon number, the estimator matched to the metric type, achieved power, multiplicity-adjusted results, and covariate-adjusted versus unadjusted estimates.
  • Three-part verdict of validity, the interval with the practical-significance threshold applied, and what would make the result conclusive - required sample and duration, the pre-registration for a rerun, or the defect to fix first - plus the cost of being wrong in each direction and no ship recommendation.
  • Pre-registration template, an automatic ratio-mismatch alarm for running experiments, sequential monitoring adopted by default so that checking results is legal, and a replication or hold-out policy for roadmap-scale claims.
You can also ask
  • the split was meant to be even and it came out 50.4 to 49.6, does that invalidate the test
  • it is flat overall but wins in one segment, can we ship it for that segment
  • we randomized by user but analyzed by session, how wrong is the confidence interval
Skills

What this agent is good at

  • Test Sample Ratio Mismatch
  • Diagnose Assignment Imbalance
  • Reconcile Randomization And Analysis Unit
  • Compute Sequential Valid Bounds
  • Control Multiple Comparisons
  • Apply Covariate Variance Reduction
  • Assess Guardrail Harm Bound
  • Detect Novelty And Interference