| --- |
| license: cc-by-4.0 |
| pretty_name: "CARD — Causal Recovery of Demand" |
| language: |
| - en |
| tags: |
| - causal-inference |
| - demand-estimation |
| - econometrics |
| - retail |
| - synthetic-data |
| - benchmark |
| size_categories: |
| - 10M<n<100M |
| configs: |
| - config_name: sample_transactions |
| data_files: dev_mini/complex_log_log_endogenous_seed001/public/transactions_train_public.csv |
| - config_name: sample_products |
| data_files: dev_mini/complex_log_log_endogenous_seed001/public/products_public.csv |
| - config_name: sample_stores |
| data_files: dev_mini/complex_log_log_endogenous_seed001/public/stores_public.csv |
| --- |
| |
| # CARD — Causal Recovery of Demand |
|
|
| **Can a model that fits observed demand well still recover causal price response, substitution, and counterfactual outcomes when prices and promotions are endogenous?** |
|
|
| CARD pairs synthetic retail scanner panels with marketing-copy product descriptions that carry the true substitution geometry. Demand is simulated from a known data-generating process; in half the cells, promotion depth responds to a hidden demand shock, so estimators that ignore endogeneity fit the observed data well and still get the counterfactuals wrong. True elasticities and counterfactual outcomes are hidden and used only for scoring. |
|
|
| **Code, submission format, scoring harness, and example notebooks:** https://github.com/jean-jsj/CARD |
|
|
| ## Quickstart |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| import pandas as pd |
| |
| # ~18 MB starter slice (10 stores); use "dev/..." for the full ~1 GB cell |
| cell = "complex_log_log_endogenous_seed001" |
| snapshot_download(repo_id="jean-jsj/CARD", repo_type="dataset", |
| allow_patterns=[f"dev_mini/{cell}/*"], local_dir="benchmark") |
| |
| train = pd.read_csv(f"benchmark/dev_mini/{cell}/public/transactions_train_public.csv") |
| products = pd.read_csv(f"benchmark/dev_mini/{cell}/public/products_public.csv") |
| ``` |
|
|
| Then score a first submission in minutes with the [GitHub quickstart](https://github.com/jean-jsj/CARD#quickstart) or the [two notebooks](https://github.com/jean-jsj/CARD/tree/main/docs/examples) (Colab-ready). A column-by-column schema is in the repo's [data dictionary](https://github.com/jean-jsj/CARD/blob/main/docs/DATA.md). |
|
|
| ## The 2×2 grid |
|
|
| | Axis | Values | |
| |---|---| |
| | Demand family | log-log demand system / structured random-coefficients discrete choice | |
| | Endogeneity | off (control) / on (promotion depth responds to a hidden demand shock; cost-based instruments stay valid) | |
|
|
| Every cell is the full market — 40 products, 731 stores — and covers 156 weeks: 140 public training weeks plus 16 holdout-context weeks whose prices/promotions are public but whose sales are withheld. The four cells are `complex_{log_log,covariance_probit}_{exogenous,endogenous}_seed001` (the `complex_` prefix is part of the frozen cell identifiers). |
|
|
| ## Layout |
|
|
| ``` |
| dev/<cell_slug>/ # full cells (~1 GB each) |
| public/ # everything a model may consume |
| transactions_train_public.csv # product, store, week, units, dollars, |
| # price, promo_flag, promo_cost, supply_cost_proxy |
| transactions_holdout_context_public.csv # holdout weeks: prices/promos public, sales withheld |
| counterfactual_sweep_context_public.csv # the 16 scored price scenarios |
| products_public.csv # product_id, product_text, brand_code |
| stores_public.csv # store_id, market, chain |
| hidden/ # DEV SEED ONLY: scoring truth for instant local scoring |
| transactions_full_hidden.csv # full 156-week panel; last 16 weeks = forecasting truth |
| elasticity_truth_hidden.csv # the true J x J elasticity matrix |
| counterfactual_sweep_truth_hidden.csv # the true counterfactual demand changes |
| release/ |
| MANIFEST.json # per-file SHA-256 |
| scoring_params.json # scoring config (family, eval window) |
| release_notes.md, DATASHEET.md |
| |
| dev_mini/<cell_slug>/ # ~18 MB 10-store starter slices (log-log pair) |
| ``` |
|
|
| **Data-access rule:** models consume `public/` files only. `hidden/` exists for local scoring on the dev seed, never as model input. Eval seeds (added later) ship public-only; their truth stays with the maintainer. |
|
|
| A `reference/` tree holds the four reference models' submission-format predictions (`reference/<model>/<cell_slug>/`, one directory per corner of the instruments × text grid); their scores and descriptions live in the GitHub repo's `submissions/` directory, and the estimator code is in its `card_metrics/baselines/` directory. A completed datasheet is at `DATASHEET.md`. |
|
|
| ## Notes |
|
|
| - **Markets and brand codes are pseudonymized** (`M01…`, `B1…`), consistently |
| across cells and seeds. |
| - The panels are **fully synthetic**, calibrated to moments of the IRI |
| academic scanner dataset; no real transactions are included. |
| - The generating code is withheld during the evaluation phase, with a |
| SHA-256 commitment to the frozen source published in the GitHub repo |
| (released after the evaluation phase). |
| - The benchmark's **actual-data arm** (validity checks on real data) uses the |
| public Dominick's Finer Foods scanner data (Kilts Center, Chicago Booth), |
| downloaded separately — see the GitHub repo. |
|
|
| ## License & citation |
|
|
| Data: CC BY 4.0. Authors: Juwon Hong, Minha Hwang, and Venkatesh Shankar. DOI: [10.57967/hf/9681](https://doi.org/10.57967/hf/9681). The associated paper reference will be added upon publication. |
|
|
| ```bibtex |
| @misc{hong2026card, |
| author = {Hong, Juwon and Hwang, Minha and Shankar, Venkatesh}, |
| title = {CARD: Causal Recovery of Demand}, |
| year = {2026}, |
| doi = {10.57967/hf/9681}, |
| publisher = {Hugging Face}, |
| url = {https://huggingface.co/datasets/jean-jsj/CARD} |
| } |
| ``` |
|
|
