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 0.9.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| dml_rs-0.9.0.tar.gz | 139.9 kB | With provenance | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| dml_rs-0.9.0-cp39-abi3-win_amd64.whl | CPython 3.9 | abi3 | Windows x86-64 | Details |
| dml_rs-0.9.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.9 | abi3 | Linux glibc 2.17+ x86-64 | Details |
| dml_rs-0.9.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl | CPython 3.9 | abi3 | Linux glibc 2.17+ ARM64 | Details |
| dml_rs-0.9.0-cp39-abi3-macosx_11_0_arm64.whl | CPython 3.9 | abi3 | macOS 11.0+ ARM64 | Details |
| dml_rs-0.9.0-cp39-abi3-macosx_10_12_x86_64.whl | CPython 3.9 | abi3 | macOS 10.12+ x86-64 | Details |
Total release size: 4.4 MB
Release files / dml_rs-0.9.0.tar.gz
| Download URL | dml_rs-0.9.0.tar.gz |
|---|---|
| Size | 139.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
9a68409da69359dce636232c78365d4ae8e1608072967639439b459e3a074169
|
|
BLAKE2b-256 checksum How to use checksums |
4902b8ffcc7594626c298caed3a35a334ec54c48f86e781cd09f180c6f0daa81
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.16
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 24, 2026.
Transparency logRelease files / dml_rs-0.9.0-cp39-abi3-win_amd64.whl
| Download URL | dml_rs-0.9.0-cp39-abi3-win_amd64.whl |
|---|---|
| Size | 749.4 kB |
| Tags | CPython 3.9 Windows x86-64 abi3 |
|
SHA-256 checksum How to use checksums |
79af0a13c9d2263c69be5bee3acfa5ad9fb22b6c179140647db8b904ac35737c
|
|
BLAKE2b-256 checksum How to use checksums |
b3229cf70555677a115d7744ac98aab53889de36fb4df44c969354206d7bb154
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.16
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 24, 2026.
Transparency logRelease files / dml_rs-0.9.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
| Download URL | dml_rs-0.9.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 941.3 kB |
| Tags | CPython 3.9 Linux glibc 2.17+ x86-64 abi3 |
|
SHA-256 checksum How to use checksums |
8f262d2fdba91e7805083942e3a5fd35630491be8bed7c8102aadec33a7828d0
|
|
BLAKE2b-256 checksum How to use checksums |
eb8838478a4803f2a683693cfc434e30c344a2b0178707c5f9a888d140d6d78f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.16
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 24, 2026.
Transparency logRelease files / dml_rs-0.9.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
| Download URL | dml_rs-0.9.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl |
|---|---|
| Size | 898.2 kB |
| Tags | CPython 3.9 Linux glibc 2.17+ ARM64 abi3 |
|
SHA-256 checksum How to use checksums |
e65dd62ed242d5a1e66e4612173e01f31f4210efdbdc537cd52e8f169a2dd39f
|
|
BLAKE2b-256 checksum How to use checksums |
f729df4f48f762da8835caae092918351a53e29f3479a080c30ab94e19f355ee
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.16
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 24, 2026.
Transparency logRelease files / dml_rs-0.9.0-cp39-abi3-macosx_11_0_arm64.whl
| Download URL | dml_rs-0.9.0-cp39-abi3-macosx_11_0_arm64.whl |
|---|---|
| Size | 828.8 kB |
| Tags | CPython 3.9 abi3 macOS 11.0+ ARM64 |
|
SHA-256 checksum How to use checksums |
35c8a003f1f5cb96ce95a8167e009d0f58d77fd69741d5a8f160ad77f0069fdd
|
|
BLAKE2b-256 checksum How to use checksums |
3080c2a611f641dc78bf01cfdeeac55faf79c9782785902d4a3875380de25ded
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.16
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 24, 2026.
Transparency logRelease files / dml_rs-0.9.0-cp39-abi3-macosx_10_12_x86_64.whl
| Download URL | dml_rs-0.9.0-cp39-abi3-macosx_10_12_x86_64.whl |
|---|---|
| Size | 877.4 kB |
| Tags | CPython 3.9 abi3 macOS 10.12+ x86-64 |
|
SHA-256 checksum How to use checksums |
10a25dab70e6e91c721038ae989201b7eae2a337d796a3670aed72f036235d14
|
|
BLAKE2b-256 checksum How to use checksums |
fc8e091b65df65530f211736dbe82cb6572c62ceb2267653f3b12b795291fde2
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.16
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 24, 2026.
Transparency logSecurity for dml-rs 0.9.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
Full provenance
All 6 files in this release have been uploaded with provenance metadata.
Attestation for dml_rs-0.9.0.tar.gz
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 24, 2026.
Transparency logA note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
