Antecedent for Python
Install from PyPI:
python -m pip install antecedent.calibratedstill requires a coverage record that attests the installed build.
Antecedent exposes the same causal contract as the Rust facade. The root namespace contains 56 names; see the naming dictionary. Start with a query and graph, identify it, estimate on data, inspect the claim, and export or refresh the study when appropriate.
import antecedent as ant
query = ant.AverageEffect("treatment", "outcome")
result = ant.analyze(data, graph=graph, query=query)
print(result.answer)
print(result.inspect().to_dict())
For large adjustment problems, 2.0 adds configured DML, DR-Learner, and causal-forest estimators. The standard wheel includes the CPU-native NeuralNet nuisance learner; no separate Python extra is needed. These estimators use held-out nuisance predictions and disclose their learner, folds, overlap, and uncertainty limitations; they do not make a CATE interval appear where none was estimated.
Structural transport uses antecedent.transport.Transport with explicit target and evidence information. Evidence availability is not inferred from a column name or a selection label.
Read the Python workflow, supported analyses, and 2.0 release notes. The support matrix remains authoritative.
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Release files for antecedent 2.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
Total release size: 222.2 MB
Release files / antecedent-2.0.0-cp314-cp314-win_amd64.whl
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Release files / antecedent-2.0.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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Release files / antecedent-2.0.0-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
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Release files / antecedent-2.0.0-cp314-cp314-macosx_11_0_arm64.whl
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