dml-rs (Python)
Fast double / debiased machine learning for causal inference, implemented in Rust and validated against DoubleML.
import dml_rs
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
# Partially linear regression with any scikit-learn learners
plr = dml_rs.PLR(ml_l=RandomForestRegressor(), ml_m=RandomForestRegressor(), n_folds=5, n_rep=3)
plr.fit(X, y, d)
print(plr.summary)
# Built-in Rust learners run the whole cross-fitting in parallel without the GIL
plr = dml_rs.PLR(ml_l=dml_rs.Lasso(alpha=0.01), ml_m=dml_rs.Lasso(alpha=0.01)).fit(X, y, d)
# Average treatment effect of a binary treatment
irm = dml_rs.IRM(ml_g=RandomForestRegressor(), ml_m=RandomForestClassifier(), score="ATE").fit(X, y, d)
# Joint inference over several treatments
plr.bootstrap(method="normal", n_boot=1000)
plr.confint(joint=True)
plr.p_adjust("romano-wolf")
See the repository for details.
1 maintainer
Metadata
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Release files for dml-rs 0.1.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
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| dml_rs-0.1.0.tar.gz | 67.3 kB | With provenance | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| dml_rs-0.1.0-cp39-abi3-win_amd64.whl | CPython 3.9 | abi3 | Windows x86-64 | Details |
| dml_rs-0.1.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.9 | abi3 | Linux glibc 2.17+ x86-64 | Details |
| dml_rs-0.1.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl | CPython 3.9 | abi3 | Linux glibc 2.17+ ARM64 | Details |
| dml_rs-0.1.0-cp39-abi3-macosx_11_0_arm64.whl | CPython 3.9 | abi3 | macOS 11.0+ ARM64 | Details |
| dml_rs-0.1.0-cp39-abi3-macosx_10_12_x86_64.whl | CPython 3.9 | abi3 | macOS 10.12+ x86-64 | Details |
Total release size: 2.9 MB
Release files / dml_rs-0.1.0.tar.gz
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