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6b67395 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | from __future__ import annotations
import json
from pathlib import Path
import joblib
import numpy as np
import pandas as pd
import trackio
from causal import estimate_effects, fit_nuisance, generate_scm
PROJECT_DIR = Path(__file__).resolve().parent
ARTIFACT_DIR = PROJECT_DIR / "artifacts" / "causal-forge"
DATA_DIR = PROJECT_DIR / "data"
REGIMES = {
"both_correct": (True, True),
"propensity_misspecified": (False, True),
"outcome_misspecified": (True, False),
"both_misspecified": (False, False),
}
ESTIMATORS = ["naive", "ipw", "outcome_regression", "aipw"]
def summarize(records: list[dict]) -> dict:
summary = {}
for regime in REGIMES:
subset = [record for record in records if record["regime"] == regime]
truth = np.asarray([record["truth"] for record in subset])
regime_summary = {}
for estimator in ESTIMATORS:
estimates = np.asarray([record[estimator] for record in subset])
errors = estimates - truth
regime_summary[estimator] = {
"mean_estimate": float(estimates.mean()),
"bias": float(errors.mean()),
"mean_absolute_error": float(np.abs(errors).mean()),
"rmse": float(np.sqrt(np.mean(errors**2))),
}
coverage = np.mean(
[
record["aipw_ci_low"] <= record["truth"] <= record["aipw_ci_high"]
for record in subset
]
)
regime_summary["aipw"]["confidence_interval_coverage"] = float(coverage)
summary[regime] = regime_summary
return summary
def save_dataset(sample) -> None:
frame = pd.DataFrame(sample.x, columns=["x1", "x2", "x3"])
frame["treatment"] = sample.treatment
frame["outcome"] = sample.outcome
frame["true_propensity"] = sample.propensity
frame["potential_outcome_control"] = sample.y0
frame["potential_outcome_treated"] = sample.y1
frame["individual_treatment_effect"] = sample.ite
DATA_DIR.mkdir(parents=True, exist_ok=True)
frame.to_parquet(DATA_DIR / "causal_benchmark.parquet", index=False)
def main() -> None:
replications = 100
samples_per_replication = 3_000
trackio.init(
project="causal-forge",
name="double-robustness-benchmark-v1",
config={
"replications": replications,
"samples_per_replication": samples_per_replication,
"cross_fitting_folds": 5,
"regimes": list(REGIMES),
},
)
records = []
for replication in range(replications):
sample = generate_scm(samples_per_replication, seed=9100 + replication)
for regime, (propensity_correct, outcome_correct) in REGIMES.items():
estimates = estimate_effects(
sample,
propensity_correct=propensity_correct,
outcome_correct=outcome_correct,
folds=5,
seed=replication,
)
records.append(
{"replication": replication, "regime": regime, **estimates}
)
if (replication + 1) % 10 == 0:
recent = records[-40:]
trackio.log(
{
"replication": replication + 1,
"aipw_recent_mae": float(
np.mean(
[
abs(record["aipw"] - record["truth"])
for record in recent
]
)
),
}
)
summary = summarize(records)
benchmark = generate_scm(20_000, seed=12043)
final_models = fit_nuisance(
benchmark.x,
benchmark.treatment,
benchmark.outcome,
propensity_correct=True,
outcome_correct=True,
)
save_dataset(benchmark)
ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
joblib.dump(final_models, ARTIFACT_DIR / "nuisance_models.joblib")
results = {
"benchmark": "Causal Forge",
"replications": replications,
"samples_per_replication": samples_per_replication,
"cross_fitting_folds": 5,
"structural_truth": "known heterogeneous individual treatment effects",
"summary": summary,
}
(ARTIFACT_DIR / "evaluation.json").write_text(
json.dumps(results, indent=2), encoding="utf-8"
)
pd.DataFrame(records).to_parquet(
ARTIFACT_DIR / "replication_estimates.parquet", index=False
)
trackio.log(
{
"both_correct_aipw_mae": summary["both_correct"]["aipw"][
"mean_absolute_error"
],
"both_correct_naive_mae": summary["both_correct"]["naive"][
"mean_absolute_error"
],
"propensity_misspecified_aipw_mae": summary[
"propensity_misspecified"
]["aipw"]["mean_absolute_error"],
"outcome_misspecified_aipw_mae": summary["outcome_misspecified"][
"aipw"
]["mean_absolute_error"],
}
)
trackio.finish()
print(json.dumps(results, indent=2))
if __name__ == "__main__":
main()
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