diff --git a/IHDP/LICENSE b/IHDP/LICENSE new file mode 100644 index 0000000..a47ea27 --- /dev/null +++ b/IHDP/LICENSE @@ -0,0 +1,65 @@ +IHDP Benchmark Covariates — License and Provenance Notice + +This directory contains `ihdp_covariates.csv`, a covariate-only extract +derived from the IHDP semi-synthetic benchmark data. + +Upstream sources and their licenses +------------------------------------ + +1. NPCI R package (vdorie/npci) + https://github.com/vdorie/npci + License: GPL (>= 2) + Role: Generated the 1000 IHDP benchmark replications from the original + IHDP trial covariates, using the response-surface design of Hill (2011). + +2. CEVAE repository (AMLab-Amsterdam/CEVAE) + https://github.com/AMLab-Amsterdam/CEVAE + License: No explicit license file in the repository (archived July 2020). + Role: Hosts the NPCI-generated replications as CSV files. The file + `ihdp_npci_1.csv` from this repository is fetched by `prep.py` at + extraction time. It is not redistributed in this repository. + +3. Original IHDP trial data + The underlying real covariates come from the Infant Health and + Development Program (IHDP), a randomized controlled trial. The + covariates were made available for research use through the benchmark + publications listed below. + +What this file contains +----------------------- + +`ihdp_covariates.csv` contains only the 25 pre-treatment covariates and +binary treatment indicator (747 rows × 26 columns). All outcome columns +(`y_factual`, `y_cfactual`, `mu0`, `mu1`) have been removed — pgmpy's +`IHDPDataset` simulator regenerates outcomes from these fixed covariates. + +The extraction script `prep.py` downloads `ihdp_npci_1.csv` from the +CEVAE repository at runtime and produces `ihdp_covariates.csv`. The +source file is not bundled here. + +Usage terms +----------- + +The NPCI simulation code is released under GPL (>= 2). The CEVAE +repository does not specify a license. The extracted covariates are +included here for research and educational use consistent with the +original benchmark's intended purpose. + +Users should: + +- Cite the original papers when using this dataset. +- Respect any terms that may apply to the upstream repositories. +- Note that this notice documents provenance and is not a substitute + for legal permission from the original data authors. + +Recommended citations +--------------------- + +- Hill, J. L. (2011). Bayesian Nonparametric Modeling for Causal + Inference. Journal of Computational and Graphical Statistics, 20(1), + 217-240. +- Louizos, C., Shalit, U., Mooij, J. M., Sontag, D., Zemel, R., & + Welling, M. (2017). Causal Effect Inference with Deep Latent-Variable + Models. NeurIPS. +- Johansson, F., Shalit, U., & Sontag, D. (2016). Learning + Representations for Counterfactual Inference. ICML. diff --git a/IHDP/README.md b/IHDP/README.md new file mode 100644 index 0000000..bd54558 --- /dev/null +++ b/IHDP/README.md @@ -0,0 +1,61 @@ +# IHDP Covariates + +This directory contains the fixed covariates and treatment assignment used by +pgmpy's IHDP semi-synthetic dataset simulator (`pgmpy.datasets.IHDPDataset`). + +## Files + +- `ihdp_covariates.csv` — covariate-only extract (747 rows × 26 columns: + `treatment` + `x1` through `x25`). +- `prep.py` — script that downloads `ihdp_npci_1.csv` from the + [CEVAE repository](https://github.com/AMLab-Amsterdam/CEVAE/tree/master/datasets/IHDP/csv), + extracts the covariates, validates the expected shape/counts/coding, + and writes `ihdp_covariates.csv`. +- `LICENSE` — provenance and licensing notice. + +## Provenance + +The extract is prepared from `ihdp_npci_1.csv` in the archived CEVAE +repository. The source file is **fetched at extraction time** by `prep.py` +(not bundled in this repo) to avoid redistributing upstream data. + +The response-surface design follows the IHDP benchmark introduced by +Hill (2011) and the later CEVAE benchmark usage from Louizos et al. (2017). +The covariates and treatment assignment are fixed across the 1000 +CEVAE/NPCI replications; only the simulated outcome columns vary. +Therefore pgmpy stores only the covariates and treatment needed to +generate outcomes inside `IHDPDataset`. + +## Reproducing `ihdp_covariates.csv` + +```bash +# Requires: numpy, pandas +python prep.py +``` + +The script downloads the source CSV, extracts covariates, runs validation +assertions, and writes `ihdp_covariates.csv`. The output is byte-identical +across runs given the same upstream file. + +## Column details + +- `x1`–`x6` (continuous): birth weight, head circumference, weeks preterm, + birth order, neonatal health index, mother's age. Already standardized + (mean ≈ 0, std ≈ 1) in the upstream CEVAE/NPCI file. +- `x7`–`x25` (binary/categorical): site and demographic indicators. All + are `{0, 1}` except `x14` ("first" — firstborn), which is `{1, 2}` by + upstream convention (matches EconML's coding). + +## References + +- Hill, J. L. (2011). Bayesian Nonparametric Modeling for Causal Inference. + *Journal of Computational and Graphical Statistics*, 20(1), 217–240. +- Louizos, C., Shalit, U., Mooij, J. M., Sontag, D., Zemel, R., & + Welling, M. (2017). Causal Effect Inference with Deep Latent-Variable + Models. *NeurIPS*. +- Johansson, F., Shalit, U., & Sontag, D. (2016). Learning Representations + for Counterfactual Inference. *ICML*. + +## License + +See [LICENSE](LICENSE) in this directory. diff --git a/IHDP/ihdp_covariates.csv b/IHDP/ihdp_covariates.csv new file mode 100644 index 0000000..e6ea17f --- /dev/null +++ b/IHDP/ihdp_covariates.csv @@ -0,0 +1,748 @@ +treatment,x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18,x19,x20,x21,x22,x23,x24,x25 +1,-0.528602821749802,-0.343454502314042,1.12855393123738,0.161702527138546,-0.316603181521744,1.29521593563369,1,0,1,0,0,0,0,1,0,1,1,1,1,0,0,0,0,0,0 +0,-1.73694491077565,-1.80200219804608,0.383827971313067,2.24431955769879,-0.62918919341276,1.29521593563369,0,0,0,1,0,0,1,1,1,1,1,1,1,0,0,0,0,0,0 +0,-0.807450996140383,-0.20294594178963,-0.360897988611247,-0.879605988141577,0.808706461285914,-0.526555605465598,0,0,0,1,0,0,0,2,0,1,0,1,1,0,0,0,0,0,0 +0,0.390083024110639,0.596582186338594,-1.85034990845987,-0.879605988141577,-0.0040171696307279,-0.857786794756378,0,0,0,0,0,1,1,2,0,1,0,1,1,0,0,0,0,0,0 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the archived CEVAE repository: +https://github.com/AMLab-Amsterdam/CEVAE/tree/master/datasets/IHDP/csv + +The source file is fetched on the fly rather than bundled in this repo +to avoid redistributing upstream data and the licensing complexity that +comes with it. The CEVAE repository is archived (read-only) since +July 2020, so the URL is stable. + +Covariates and treatment are identical across all 1000 CEVAE/NPCI +replications (only the simulated outcome columns y_factual, y_cfactual, +mu0, mu1 vary), so extracting from replication #1 is sufficient and +representative of every other replication. + +This file becomes permanent shared infrastructure once uploaded to the +pgmpy/example_datasets HuggingFace repo and loaded via _get_raw_data() +on every IHDPDataset instantiation, so the assertions below are meant +to catch a bad extraction (wrong source file, corrupted download, +upstream format change) before it ships, not just document intent. + +Usage: + python prep.py + +Requirements: + numpy, pandas +""" + +import os +import ssl +import subprocess +import tempfile +import urllib.request + +import numpy as np +import pandas as pd + +CEVAE_URL = ( + "https://raw.githubusercontent.com/AMLab-Amsterdam/CEVAE/" + "master/datasets/IHDP/csv/ihdp_npci_1.csv" +) +OUTPUT_PATH = "ihdp_covariates.csv" + +# Download source file + + +def _download(url, dest): + """Download ``url`` to ``dest``, working around common SSL issues.""" + # Try default SSL context first. + try: + urllib.request.urlretrieve(url, dest) + return + except urllib.error.URLError: + pass + + # Try certifi certificates if available. + try: + import certifi + + ctx = ssl.create_default_context(cafile=certifi.where()) + opener = urllib.request.build_opener(urllib.request.HTTPSHandler(context=ctx)) + with opener.open(url) as resp, open(dest, "wb") as f: + f.write(resp.read()) + return + except (ImportError, urllib.error.URLError): + pass + + # Last resort: fall back to curl (available on macOS/Linux). + result = subprocess.run(["curl", "-fsSL", "-o", dest, url], capture_output=True) + if result.returncode == 0: + return + + raise RuntimeError( + f"Could not download {url}. Tried urllib, certifi, and curl. " + "Check your network connection and SSL certificates." + ) + + +print(f"Downloading {CEVAE_URL} ...") +tmp_fd, tmp_path = tempfile.mkstemp(suffix=".csv") +try: + os.close(tmp_fd) + _download(CEVAE_URL, tmp_path) + print(f"Downloaded to temporary file: {tmp_path}") + + # Load and validate + + cols = ["treatment", "y_factual", "y_cfactual", "mu0", "mu1"] + [f"x{i}" for i in range(1, 26)] + df = pd.read_csv(tmp_path, header=None, names=cols) +finally: + if os.path.exists(tmp_path): + os.unlink(tmp_path) + + print("Cleaned up temporary file.") + +print(f"Original shape: {df.shape}") +assert df.shape == (747, 30), f"Expected (747, 30), got {df.shape}" + +# Extract covariates + treatment +covariates = df[["treatment"] + [f"x{i}" for i in range(1, 26)]] + +print(f"Extracted shape: {covariates.shape}") +assert covariates.shape == (747, 26), f"Expected (747, 26), got {covariates.shape}" + +n_treated = int(covariates["treatment"].sum()) +n_control = len(covariates) - n_treated +print(f"Treated count: {n_treated}") +print(f"Control count: {n_control}") +assert (n_treated, n_control) == (139, 608), ( + f"Expected 139 treated / 608 control (the standard post-exclusion IHDP sample), got {n_treated} / {n_control}" +) + +# x1-x6 (bw, b.head, preterm, birth.o, nnhealth, momage) are continuous +# and, per the upstream NPCI/CEVAE pipeline, already standardized. This +# is what lets the simulator skip re-standardizing on every load. +continuous_cols = [f"x{i}" for i in range(1, 7)] +means = covariates[continuous_cols].mean() +stds = covariates[continuous_cols].std() +assert np.allclose(means, 0, atol=1e-8), f"x1-x6 means not ~0:\n{means}" +assert np.allclose(stds, 1, atol=1e-6), f"x1-x6 stds not ~1:\n{stds}" +print("Continuous columns x1-x6 confirmed pre-standardized (mean~0, std=1).") + +# x7-x25 are binary/categorical site & demographic indicators. All are +# {0,1} except x14 ("first" = firstborn indicator), which is {1,2} in +# the canonical CEVAE/NPCI data — not a bug: EconML's own port carries +# an explicit comment doing the equivalent {0,1}->{1,2} shift for this +# same variable, so this is the literature-standard coding, kept as-is. +strictly_binary_cols = [f"x{i}" for i in range(7, 26) if i != 14] +for col in strictly_binary_cols: + uniques = set(covariates[col].unique().tolist()) + assert uniques <= {0, 1}, f"{col} is not binary, found values: {uniques}" +assert set(covariates["x14"].unique().tolist()) == {1, 2}, ( + "x14 ('first') expected to be {1,2}-coded (matches EconML's convention " + "for this variable) — got a different value set, upstream file may " + "have changed." +) +print("Columns x7-x25 confirmed binary (0/1), except x14 ('first') which is {1,2} by design.") + +covariates.to_csv(OUTPUT_PATH, index=False) +print(f"Saved {OUTPUT_PATH}") + +# Covariate identity reference (documentation only -- x1..x25 stay as the +# actual column names; see rationale below the dict). +# +# Verified empirically, not assumed from documentation: cross-referenced +# against EconML's independently-maintained raw covariate file +# (econml/data/ihdp/sim.csv, github.com/microsoft/EconML), which has real +# column names. Confirmed by reconstruction, not just plausible ordering: +# 1. Applied the same treated-nonwhite-mother exclusion to EconML's raw +# 985-row file -> 747 rows, 139 treated / 608 control (exact match) +# 2. Row-by-row treatment assignment: 100% match after exclusion, which +# only happens if row order is identical between the two independently +# sourced files (a random misalignment would give ~70% agreement, not +# 100%, given the 139/747 treated base rate) +# 3. x7-x25 (all binary/site indicators except x14): bit-for-bit exact +# match against EconML's named columns, in this order +# 4. x14 ("first"): exact match once EconML's raw {0,1} value has 1 +# added -> {1,2} -- confirming this is genuinely how the variable is +# coded upstream, not a data error introduced anywhere in this pipeline +# 5. x1-x6 (continuous): exact match, residuals at floating-point +# precision (1e-9 to 1e-15), once EconML's raw values are standardized +# using this file's own 747-row mean and std(ddof=1) +# +# Every one of treatment + x1..x25 reconstructs exactly under this mapping. +# +# Not renamed to these names in the actual output: every paper and package +# that reports IHDP results (NPCI, EconML, CEVAE, ...) refers to these +# covariates as x1-x25, and IHDPDataset's DAG/test/cross-validation code +# is built around that convention. This dict is for anyone who needs to +# know what a given xN actually is. + +COVARIATE_INFO = { + "x1": ("bw", "continuous", "Birth weight (grams)"), + "x2": ("b.head", "continuous", "Head circumference at birth"), + "x3": ("preterm", "continuous", "Weeks preterm"), + "x4": ("birth.o", "continuous", "Birth order"), + "x5": ("nnhealth", "continuous", "Neonatal health index"), + "x6": ("momage", "continuous", "Mother's age"), + "x7": ("sex", "binary", "Infant sex"), + "x8": ("twin", "binary", "Twin birth"), + "x9": ("b.marr", "binary", "Mother married at birth"), + "x10": ("mom.lths", "binary", "Mother's education: less than high school"), + "x11": ("mom.hs", "binary", "Mother's education: high school"), + "x12": ("mom.scoll", "binary", "Mother's education: some college"), + "x13": ("cig", "binary", "Smoked cigarettes during pregnancy"), + "x14": ("first", "{1,2} -- not 0/1, see note above", "Firstborn"), + "x15": ("booze", "binary", "Drank alcohol during pregnancy"), + "x16": ("drugs", "binary", "Used drugs during pregnancy"), + "x17": ("work.dur", "binary", "Worked during pregnancy"), + "x18": ("prenatal", "binary", "Received prenatal care"), + "x19": ("site1", "binary", "Trial site indicator"), + "x20": ("site2", "binary", "Trial site indicator"), + "x21": ("site3", "binary", "Trial site indicator"), + "x22": ("site4", "binary", "Trial site indicator"), + "x23": ("site5", "binary", "Trial site indicator"), + "x24": ("site6", "binary", "Trial site indicator"), + "x25": ("site7", "binary", "Trial site indicator"), +} diff --git a/README.md b/README.md index 7088217..873767f 100644 --- a/README.md +++ b/README.md @@ -7,3 +7,7 @@ The licences are included in the respective dataset folders as well. 2. [nslm](https://github.com/grf-labs/grf/tree/master/experiments/acic18): CC0 1.0 Universal. Last downloaded on 2026-03-04. 3. [Tuebingen-pair-wise-dataset](https://webdav.tuebingen.mpg.de/cause-effect/): Last downloaded on 2026-03-02. 4. [Twins-datasets](http://www.nber.org/data/linked-birth-infant-death-data-vital-statistics-data.html) +5. [IHDP covariates](IHDP/README.md): Derived from the NPCI-generated (GPL ≥ 2) IHDP benchmark + replications hosted in the archived CEVAE repository. Covariates only — outcomes are + regenerated by pgmpy's simulator. See [IHDP/LICENSE](IHDP/LICENSE) for full provenance. + Added on 2026-07-06.