CE Mondrian Conditional

You are setting up Mondrian (conditional) calibration, which partitions calibration data into subgroups so that each group receives its own uncertainty estimate. This reveals group-specific prediction quality and is the foundational technique for fairness-aware deployments in CE.

Research: Conditional Calibrated Explanations (xAI 2024)

Load references/mondrian_examples.md for full code examples (Options A/B/C, fairness analysis, global vs conditional comparison).

Why Mondrian matters for fairness

Without conditional calibration, the CPS/Venn-Abers calibrator averages over all calibration instances. A minority group with harder prediction patterns may receive the same interval width as an easy majority group, hiding bias.

Mondrian splits calibration by a grouping key and fits a separate calibrator per bin. Resulting intervals are:

• Narrower for groups the model predicts reliably.
• Wider for groups the model predicts poorly.

Three options for specifying bins

• Option A — Inline bins array: pass integer group labels directly at calibrate time.
• Option B — MondrianCategorizer (recommended for continuous features): auto-bins via crepes.extras.MondrianCategorizer.
• Option C — Lambda as mc: pass a callable directly for ad-hoc one-off scripts.

Calibration -> predict -> explain consistency rules

CRITICAL: always pass bins= at inference time whenever the explainer was calibrated with Mondrian bins. Omitting it silently falls back to global calibration, which defeats fairness analysis.

Minimum bin size warning

Mondrian calibration splits the calibration set by group. Too few samples per bin leads to unreliable or very wide intervals.

Rule of thumb: aim for >= 30-50 calibration samples per bin.

Out of Scope

• DifficultyEstimator (per-instance sigma scaling for regression; see ce-regression-intervals).
• Reject policies (deciding whether to defer uncertain predictions; see ce-reject-policy).
• Fairness constraint enforcement (CE reveals uncertainty; it does not enforce fairness automatically).

Evaluation Checklist

• Group labels at explain/predict time match the label space used at calibrate time.
• Minimum bin size verified (>= 30 samples per bin recommended).
• Both calibrate(mc=...) and explain_factual(bins=...) consistently use the same categorizer.
• If comparing global vs Mondrian: separate WrapCalibratedExplainer instances used.
• Per-group interval widths inspected to surface differential uncertainty.
• Coverage rate verified per group (not just pooled).