clinical-cad 0.1.1

Robust Clinical Association Displacement (CAD) analysis with statistical normalization

clinical-cad

Clinical Association Displacement (CAD) and Weighted Association Error (WAE) for lexical fairness over any binary demographic (sex, race, age group, etc.). Compare a reference corpus (e.g. ground-truth text) with model predictions to see how word associations with two groups change, and get a single fairness number (WAE).

---

Installation

pip install clinical-cad

---

Quick example

1. Run with your own CSVs (reference = ground truth, prediction = model output):

cad --reference_csv reference.csv --prediction_csv predictions.csv

Results go to ./cad_output by default. Use --output_dir my_results to change that.

2. Try it without data (demo):

cad --demo

3. Use a different demographic (e.g. race instead of sex):

Your CSV must have a column with exactly two values (e.g. White and Black). Then:

cad --reference_csv ref.csv --prediction_csv pred.csv \
    --label_column race --group_a White --group_b Black

---

What it does (for new users)

• CAD
  For each word, it measures how strongly it’s associated with group A vs group B in the reference corpus, and how that changes in the prediction corpus. Words that shift a lot are “displaced” and can indicate bias.

• WAE (Weighted Association Error)
  One number summarizing how much associations shifted overall. Lower WAE = fairer (less lexical bias). It comes with 95% confidence intervals (bootstrap).

You get JSON summaries, CSV tables, and plots in the output directory.

---

Input format

Both CSVs need:

1. A text column (default: findings for reference, predicted_report for predictions; override with --reference_text_column / --prediction_text_column).
2. A demographic column with exactly two values (e.g. M/F, White/Black, young/old). Default column name is PatientSex if you don’t pass --label_column.

Example:

PatientSex	findings
M	No acute findings.
F	Normal heart size.

---

Parameters (only the important ones)

Parameter	Default	Why it matters
--reference_csv	(required)	Path to reference (ground-truth) CSV.
--prediction_csv	(required)	Path to model-output CSV.
--output_dir	./cad_output	Where to write results.
--label_column	PatientSex	Column that holds the two groups (e.g. sex, race).
--group_a	F	First group value (e.g. F, White).
--group_b	M	Second group value (e.g. M, Black).
--alpha	0.1	Smoothing for rare words; keeps log-odds stable.
--min_freq	1	Ignore words with fewer than this many occurrences.
--significance_level	0.0455	P-value for “strong” association; lower = stricter.
--neutral_significance	0.317	P-value for “neutral”; higher = more words treated as neutral.
--displacement_significance	0.01	P-value for when a word is considered “displaced”.
--wae_weight	total	How to weight words: total, ref, or pred.
--bootstrap_samples	1000	Number of bootstrap samples for WAE confidence intervals.
--stopwords_path	—	Optional file with extra stopwords (one per line).
--no_stopword_removal	False	Turn off stopword removal.
--demo	False	Run on built-in demo data (no CSVs needed).

---

Paper and citation

This tool supports the methodology described in our paper on lexical fairness in clinical text (e.g. radiology report generation).

• Paper: https://arxiv.org/abs/2603.01625
  (Replace with your actual URL.)

If you use clinical-cad in your work, please cite:

@article{parikh2026measuring,
  title={Measuring What VLMs Don't Say: Validation Metrics Hide Clinical Terminology Erasure in Radiology Report Generation},
  author={Parikh, Aditya and Feragen, Aasa and Das, Sneha and Frank, Stella},
  journal={arXiv preprint arXiv:2603.01625},
  year={2026}
}

(Replace title, author, booktitle, year, and url with your paper details.)

---

Python API

from clinical_cad import parse_args, run_pipeline

args = parse_args()
run_pipeline(
    args.reference_csv,
    args.reference_text_column,
    args.prediction_csv,
    args.prediction_text_column,
    args.label_column or "PatientSex",
    args.output_dir,
    args,
)

---

License

MIT