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

Step	Bins argument
calibrate(...)	mc= (MondrianCategorizer or callable) OR bins= (integer array)
predict(x, ...)	nothing if mc was used at calibrate time OR bins=group_labels_test
predict_proba(x, ...)	same as above
explain_factual(x, ...)	same as above
explore_alternatives(x, ...)	same as above

CRITICAL: always pass bins= at inference time whenever the explainer was calibrated with inline Mondrian bins. Omitting it raises ValidationError; passing bins= after global calibration raises ConfigurationError. When mc= was used at calibration time, omit bins= at inference because the stored categorizer derives labels automatically.

Recalibrating without bins= or mc= resets to global calibration. Use calibrate(..., reuse_conditional=True) only when intentionally reusing a stored categorizer on a new calibration set.

---

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).
• Inline bins= were supplied at calibration and inference, or mc= was supplied at calibration and inference omits bins=.
• 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).