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Methods, validation, and explainability

This page describes the shape of the approach. The detailed methodology note — estimation design, validation protocol, and the full explainability specification — is shared privately with methodologists under agreement, because it is the level of detail your staff will want to argue with.

The approach, at capability level

  • Machine-learning reconstruction with uncertainty on every figure. Values are estimated at record level and by category, independently of what the field recorded. No figure is delivered without a quantified uncertainty interval — a point estimate alone is not a usable input to a statistical decision.
  • Aggregation aligned to international statistical standards. Category structures follow the classification standards your office already reports against, so output can be read alongside your published tables rather than translated into them.
  • Logical-consistency rule sets, configured by your staff. Consistency rules are calibrated to the instrument and set by your methodologists. We do not import a fixed rule set and call it good practice.
  • Reconciliation against published national aggregates. Reconstructions are reconciled against national totals your office has already published, so the independent reading stays anchored to the official record.
  • Explanations at model level and record level. Every estimate carries an account of what drove it. Methodologists interrogate the reasoning; they are not asked to accept a number.
  • Documented to fit your governance framework. Documented to sit within the standard statistical business process stages and the international principles governing official statistics.

Validation

Checked against your ground truth, not against our claims

Validation reporting is produced per deployment, against the office's own ground truth — a round it has already collected and published. That is the point of reconstructing rather than predicting: the accuracy of the method is a fact your office can establish for itself, on its own data, before relying on it.

We publish no accuracy figures on this site. A number produced on someone else's data, on a different instrument, in a different country, would tell you nothing about your own — and we would rather say that than quote one.

What makes it reviewable

Traceability

Every figure carries its provenance

Inputs, configuration, model version, uncertainty, and explanation travel with each estimate. A figure you cannot trace is a figure you cannot defend in public, and official statistics are defended in public for years.

Explainability

Reasoning, not just results

Explanations are produced at model and record level as a standard deliverable. They exist so your methodologists can disagree with the platform on substantive grounds.

Human decision

The platform proposes; the office disposes

Output is a ranked list of divergences for analyst review. No estimate is published, substituted, or acted on without a decision by your staff, and every decision is logged.

Governance

Documented against frameworks you already answer to

The approach is documented to map onto the standard statistical business process stages and the international principles governing official statistics.

Stated limits

What the method cannot do

  • It cannot create signal that is not there. Where no administrative or macroeconomic signal exists for a quantity, reconstruction has nothing independent to work from, and the report says so rather than producing a confident number.
  • It cannot adjudicate a divergence. A divergence means the two readings disagree. Which one is wrong is a methodological judgement, and it stays with your staff.
  • It cannot replace field collection. The survey remains the ground truth. Remove it and there is nothing to reconstruct against.

Request the technical methodology note

The full note — estimation design, validation protocol, explainability specification — is shared with methodologists under agreement.