Missingness

Controlled injection of missing values into generated datasets.

Use missingness workflows to inject deterministic synthetic null patterns for robustness testing under MCAR, MAR, and MNAR regimes.

When to use

  • You want realistic training/evaluation with incomplete tabular data.
  • You need controlled ablations across missingness mechanisms.
  • You want benchmark guardrails to include missingness-aware checks.

Preset workflows

Use presets for standard mechanism runs:

dagzoo generate --config configs/preset_missingness_mcar.yaml --num-datasets 25 --out data/run_missing_mcar
dagzoo generate --config configs/preset_missingness_mar.yaml --num-datasets 25 --out data/run_missing_mar
dagzoo generate --config configs/preset_missingness_mnar.yaml --num-datasets 25 --out data/run_missing_mnar

Targeted MAR calibration via CLI

dagzoo generate \
  --config configs/default.yaml \
  --num-datasets 25 \
  --device cpu \
  --missing-rate 0.25 \
  --missing-mechanism mar \
  --missing-mar-observed-fraction 0.6 \
  --missing-mar-logit-scale 1.4 \
  --out data/run_missing_cli_mar

Key options

  • --missing-rate: overall missingness probability.
  • --missing-mechanism: mcar, mar, or mnar.
  • --missing-mar-observed-fraction: fraction of observed features used for MAR logits.
  • --missing-mar-logit-scale: MAR logit sensitivity multiplier.

What to inspect

  • metadata.ndjson dataset records for resolved missingness configuration.
  • Benchmark summaries for missingness_guardrails (when present).

For output details, see output-format.md.