معرفی شرکت ها
DoubleML-0.5.0
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Double Machine Learning in Python
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | DoubleML-0.5.0 |
| نام | DoubleML |
| نسخه کتابخانه | 0.5.0 |
| نگهدارنده | ['Malte S. Kurz'] |
| ایمیل نگهدارنده | ['malte.simon.kurz@uni-hamburg.de'] |
| نویسنده | Bach, P., Chernozhukov, V., Kurz, M. S., and Spindler, M. |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://docs.doubleml.org |
| آدرس اینترنتی | https://pypi.org/project/DoubleML/ |
| مجوز | - |
# DoubleML - Double Machine Learning in Python <a href="https://docs.doubleml.org"><img src="https://raw.githubusercontent.com/DoubleML/doubleml-for-py/master/doc/logo.png" align="right" width = "120" /></a>
[](https://github.com/DoubleML/doubleml-for-py/actions?query=workflow%3Abuild)
[](https://badge.fury.io/py/DoubleML)
[](https://anaconda.org/conda-forge/doubleml)
[](https://codecov.io/gh/DoubleML/doubleml-for-py)
[](https://www.codacy.com/gh/DoubleML/doubleml-for-py/dashboard?utm_source=github.com&utm_medium=referral&utm_content=DoubleML/doubleml-for-py&utm_campaign=Badge_Grade)
[](https://www.python.org/)
The Python package **DoubleML** provides an implementation of the double / debiased machine learning framework of
[Chernozhukov et al. (2018)](https://doi.org/10.1111/ectj.12097).
It is built on top of [scikit-learn](https://scikit-learn.org) (Pedregosa et al., 2011).
Note that the Python package was developed together with an R twin based on [mlr3](https://mlr3.mlr-org.com/).
The R package is also available on [GitHub](https://github.com/DoubleML/doubleml-for-r) and
[](https://cran.r-project.org/package=DoubleML).
## Documentation and Maintenance
Documentation and website: [https://docs.doubleml.org/](https://docs.doubleml.org/)
**DoubleML** is currently maintained by
[@MalteKurz](https://github.com/MalteKurz) and
[@PhilippBach](https://github.com/PhilippBach).
Bugs can be reported to the issue tracker at
[https://github.com/DoubleML/doubleml-for-py/issues](https://github.com/DoubleML/doubleml-for-py/issues).
## Main Features
Double / debiased machine learning [(Chernozhukov et al. (2018))](https://doi.org/10.1111/ectj.12097) for
- Partially linear regression models (PLR)
- Partially linear IV regression models (PLIV)
- Interactive regression models (IRM)
- Interactive IV regression models (IIVM)
The object-oriented implementation of DoubleML is very flexible.
The model classes `DoubleMLPLR`, `DoubleMLPLIV`, `DoubleMLIRM` and `DoubleIIVM` implement the estimation of the nuisance
functions via machine learning methods and the computation of the Neyman orthogonal score function.
All other functionalities are implemented in the abstract base class `DoubleML`.
In particular functionalities to estimate double machine learning models and to perform statistical inference via the
methods `fit`, `bootstrap`, `confint`, `p_adjust` and `tune`.
This object-oriented implementation allows a high flexibility for the model specification in terms of ...
- ... the machine learners for the nuisance functions,
- ... the resampling schemes,
- ... the double machine learning algorithm,
- ... the Neyman orthogonal score functions,
- ...
It further can be readily extended with regards to
- ... new model classes that come with Neyman orthogonal score functions being linear in the target parameter,
- ... alternative score functions via callables,
- ... alternative resampling schemes,
- ...
## Installation
**DoubleML** requires
- Python
- sklearn
- numpy
- scipy
- pandas
- statsmodels
- joblib
To install DoubleML with pip use
```
pip install -U DoubleML
```
DoubleML can be installed from source via
```
git clone git@github.com:DoubleML/doubleml-for-py.git
cd doubleml-for-py
pip install --editable .
```
Detailed [installation instructions](https://docs.doubleml.org/stable/intro/install.html) can be found in the documentation.
## Contributing
DoubleML is a community effort.
Everyone is welcome to contribute.
To get started for your first contribution we recommend reading our
[contributing guidelines](https://github.com/DoubleML/doubleml-for-py/blob/master/CONTRIBUTING.md)
and our
[code of conduct](https://github.com/DoubleML/doubleml-for-py/blob/master/CODE_OF_CONDUCT.md).
## Citation
If you use the DoubleML package a citation is highly appreciated:
Bach, P., Chernozhukov, V., Kurz, M. S., and Spindler, M. (2022), DoubleML - An
Object-Oriented Implementation of Double Machine Learning in Python,
Journal of Machine Learning Research, 23(53): 1-6,
[https://www.jmlr.org/papers/v23/21-0862.html](https://www.jmlr.org/papers/v23/21-0862.html).
Bibtex-entry:
```
@article{DoubleML2022,
title = {{DoubleML} -- {A}n Object-Oriented Implementation of Double Machine Learning in {P}ython},
author = {Philipp Bach and Victor Chernozhukov and Malte S. Kurz and Martin Spindler},
journal = {Journal of Machine Learning Research},
year = {2022},
volume = {23},
number = {53},
pages = {1--6},
url = {http://jmlr.org/papers/v23/21-0862.html}
}
```
## References
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W. and Robins, J. (2018),
Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21: C1-C68. doi:[10.1111/ectj.12097](https://doi.org/10.1111/ectj.12097).
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011),
Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12: 2825--2830, [https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html](https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html).
نیازمندی
| مقدار | نام |
|---|---|
| - | joblib |
| - | numpy |
| - | pandas |
| - | scipy |
| - | sklearn |
| - | statsmodels |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl DoubleML-0.5.0:
pip install DoubleML-0.5.0.whl
نصب پکیج tar.gz DoubleML-0.5.0:
pip install DoubleML-0.5.0.tar.gz