Datasets:

jean-jsj
/

CARD

For what purpose was the dataset created? To test whether demand models that fit observed sales well can also recover causal price response, substitution structure, and counterfactual outcomes when prices and promotions are endogenous. The benchmark pairs a synthetic retail transaction panel with product description texts that carry the true substitution geometry, so it also measures whether a model extracts economic structure from text.

Who created it? Juwon Hong, Minha Hwang, and Venkatesh Shankar; maintained by Juwon Hong.

Composition

What do the instances represent? Store-product-week transaction records from a synthetic facial-tissue category, plus one marketing-copy description per product. The data are generated by a known data-generating process; no real transactions are included.

Structure. A factorial grid of 4 cells (2 demand-model families × endogeneity on/off). Every cell is the full market: 40 products, 731 stores, 156 weeks (140 public training weeks + 16 holdout-context weeks with quantities withheld).

Files per cell (public). transactions_train_public.csv and transactions_holdout_context_public.csv (product_id, store_id, week, units, dollars, price, promo_flag, promo_cost, supply_cost_proxy; quantities withheld in the holdout context), products_public.csv (product_id, product_text, brand_code), stores_public.csv (store_id, market, chain), and counterfactual_sweep_context_public.csv (intervention_id, join keys, baseline_price, intervention_price). Market and brand identifiers are pseudonymized; store demographics are withheld. The authoritative enumeration with per-file SHA-256 hashes is each cell's release/MANIFEST.json.

Is anything withheld? Yes, deliberately: the substitution answer key, the hidden ground-truth elasticities and counterfactual outcomes, the hidden full-length panel, all structural shocks, and the store demographics. The dev seed ships its scoring truth (hidden/) for local iteration; eval-seed truth stays with the maintainer. The generator is withheld during the evaluation phase under a published SHA-256 commitment (GENERATOR_COMMITMENT.md).

Does the dataset contain personal data, confidential data, or offensive content? No. All records are synthetic; the texts are machine-written marketing copy for facial tissue. Store identifiers are synthetic; market and brand codes are pseudonymized. No human subjects, no PII.

Collection process (generation process)

There is no collection process; the dataset is simulated. The numeric generator is calibrated to moments of the licensed IRI academic retail panel and TDLinx/Spectra store demographics, plus public CDC FluView influenza curves; the licensed sources enter only through aggregate calibration targets and per-product anchors whose publication their licenses permit. The product texts were written by a large language model from controlled per-product briefs and frozen with a published checksum. The substitution answer key is built from the texts by a committee of four pinned sentence encoders. The construction is documented at the equation level in the paper appendix.

Preprocessing / cleaning / labeling

Released prices are rounded to realistic price endings; observed counts reflect permanent store assortments plus a missing-not-at-random zero mechanism matching retail sparsity (public files carry positive-sale rows only). Causal answer keys are computed on the carried support before those observation layers, so the ground truth is unaffected. No other cleaning is applied.

Uses

Intended. Benchmarking demand models: sales forecasting on the holdout window, elasticity recovery, counterfactual demand response under price interventions, and the value of reading the product text. Classical econometric, ML, and multimodal/foundation-model approaches are all in scope.

Out of scope. The data support no conclusions about the real facial-tissue market, any real retailer or brand, or consumer behavior; the texts are not a general product-text corpus; the panel is not suitable for training production pricing systems.

Distribution

Data: Hugging Face jean-jsj/CARD, CC BY 4.0, archived under DOI 10.57967/hf/9681. Evaluation and scoring code: github.com/jean-jsj/CARD, Apache-2.0.

Maintenance

Maintainer and contact: Juwon Hong (GitHub: jean-jsj; issues on the code repository). Versioning: releases are immutable and identified by the benchmark_version stamp in each cell's release/scoring_params.json; corrections publish as a new dataset version, never as silent overwrites. Scoring math is frozen between minor versions of the card-metrics package, so scores stay comparable. Hidden evaluation-seed truth is disclosed when a seed is retired and is verifiable against the published commitments.