dml-rs
Fast double / debiased machine learning for causal inference, implemented in Rust.
dml-rs implements the double / debiased machine learning framework of Chernozhukov et al. (2018) with a scikit-learn style API. Every model is tested for numerical agreement with the reference DoubleML package, and the Rust core makes estimation typically 3–24× faster.
Installation
pip install dml-rs
Prebuilt wheels are available for Linux, macOS and Windows (Python 3.9 or newer).
Quick start
import numpy as np
import dml_rs
# Simulated data: the true effect of d on y is 0.5; X confounds both
rng = np.random.default_rng(42)
n = 2000
X = rng.normal(size=(n, 5))
d = np.sin(X[:, 0]) + X[:, 1] ** 2 + rng.normal(size=n)
y = 0.5 * d + np.cos(X[:, 0]) + X[:, 1] ** 2 + rng.normal(size=n)
model = dml_rs.PLR(
ml_l=dml_rs.RandomForestRegressor(min_samples_leaf=5), # learner for E[y | X]
ml_m=dml_rs.RandomForestRegressor(min_samples_leaf=5), # learner for E[d | X]
n_folds=5,
)
model.fit(X, y, d)
print(model.summary)
coef std err t P>|t| 2.5 % 97.5 %
d 0.516919 0.029474 17.538215 7.319031e-69 0.459151 0.574687
Any scikit-learn estimator works as a learner. The built-in learners
(GradientBoostingRegressor, RandomForestRegressor, Lasso, LassoCV, Ols,
LogisticRegression, LogisticRegressionCV, and the corresponding classifiers) run the whole estimation in Rust, in parallel and without the GIL.
Models
| Class | Target parameter |
|---|---|
PLR |
Partially linear regression: effect of a continuous or binary treatment |
IRM |
Interactive regression model: ATE / ATTE of a binary treatment; cate and gate |
PLIV |
Partially linear IV regression |
IIVM |
Interactive IV model: LATE with a binary instrument |
DID, DIDCS |
Difference-in-differences for panels and repeated cross-sections |
StaggeredDID |
Staggered adoption: ATT(g, t) with group, time and event-study aggregation |
SLearner, TLearner, XLearner, RLearner, DRLearner |
Heterogeneous (conditional average) treatment effects |
All estimators support repeated sample splitting, multiplier bootstrap with simultaneous
confidence bands (bootstrap, confint(joint=True)), multiple-testing adjustment (p_adjust),
pandas summaries, and the scikit-learn parameter protocol (get_params, set_params, clone).
PLR, IRM and DID models also offer sensitivity analysis for omitted confounders
(sensitivity_analysis, sensitivity_summary, sensitivity_benchmark). Pass cluster= to
fit for cluster-robust inference with one- or two-way clustered data, and inspect the nuisance
learners with nuisance_loss_ and evaluate_learners.
Documentation
Full feature overview, benchmarks and validation details: https://github.com/AsadGudzevic94/dml-rs
License
Licensed under either of the Apache License 2.0 or the MIT license, at your option.
1 maintainer
Metadata
1 maintainer
Release files for dml-rs 0.11.0
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Built distributions (wheels)
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| dml_rs-0.11.0-cp39-abi3-win_amd64.whl | CPython 3.9 | abi3 | Windows x86-64 | Details |
| dml_rs-0.11.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.9 | abi3 | Linux glibc 2.17+ x86-64 | Details |
| dml_rs-0.11.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl | CPython 3.9 | abi3 | Linux glibc 2.17+ ARM64 | Details |
| dml_rs-0.11.0-cp39-abi3-macosx_11_0_arm64.whl | CPython 3.9 | abi3 | macOS 11.0+ ARM64 | Details |
| dml_rs-0.11.0-cp39-abi3-macosx_10_12_x86_64.whl | CPython 3.9 | abi3 | macOS 10.12+ x86-64 | Details |
Total release size: 5.3 MB
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