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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
# DoubleML - Double Machine Learning in R <a href="https://docs.doubleml.org"><img src="man/figures/logo.png" align="right" width = "120" /></a>
[](https://github.com/DoubleML/doubleml-for-r/actions?query=workflow%3Abuild)
[](https://app.codecov.io/gh/DoubleML/doubleml-for-r/)
[](https://www.codefactor.io/repository/github/doubleml/doubleml-for-r)
[](https://cran.r-project.org/package=DoubleML)
The R package **DoubleML** provides an implementation of the double / debiased machine learning framework of [Chernozhukov et al. (2018)](https://arxiv.org/abs/1608.00060). It is built on top of [mlr3](https://mlr3.mlr-org.com/) and the [mlr3 ecosystem](https://github.com/mlr-org/mlr3/wiki/Extension-Packages) (Lang et al., 2019).
Note that the R package was developed together with a python twin based on [scikit-learn](https://scikit-learn.org/). The python package is also available on [GitHub](https://github.com/DoubleML/doubleml-for-py) and [](https://badge.fury.io/py/DoubleML).
## Documentation and maintenance
Documentation of functions in R: [https://docs.doubleml.org/r/stable/reference/index.html](https://docs.doubleml.org/r/stable/reference/index.html)
User guide: [https://docs.doubleml.org](https://docs.doubleml.org)
**DoubleML** is currently maintained by [`@PhilippBach`](https://github.com/PhilippBach) and [`@SvenKlaassen`](https://github.com/SvenKlaassen).
## Main Features
Double / debiased machine learning framework of [Chernozhukov et al. (2018)](https://arxiv.org/abs/1608.00060) 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** that is based on the [R6 package for R](https://r6.r-lib.org/) 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 learning methods for estimation of 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
Install the latest release from CRAN:
```{r, eval = FALSE}
remotes::packages("DoubleML")
```
Install the development version from GitHub:
```{r, eval = FALSE}
remotes::install_github("DoubleML/doubleml-for-r")
```
**DoubleML** requires
* R (>= 3.5.0)
* R6 (>= 2.4.1)
* data.table (>= 1.12.8)
* stats
* checkmate
* mlr3 (>= 0.5.0)
* mlr3tuning (>= 0.3.0)
* mlr3learners (>= 0.3.0)
* mvtnorm
* utils
* clusterGeneration
* readstata13
## 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-r/blob/main/CONTRIBUTING.md)
and our
[code of conduct](https://github.com/DoubleML/doubleml-for-r/blob/main/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. (2021),
DoubleML - An Object-Oriented Implementation of Double Machine Learning in R,
arXiv:[2103.09603](https://arxiv.org/abs/2103.09603).
Bibtex-entry:
```
@misc{DoubleML2020,
title={{DoubleML} -- {A}n Object-Oriented Implementation of Double Machine Learning in {R}},
author={P. Bach and V. Chernozhukov and M. S. Kurz and M. Spindler and Sven Klaassen},
year={2024},
journal={Journal of Statistical Software},
volume={108},
number={3},
pages= {1-56},
doi={10.18637/jss.v108.i03},
note={arXiv:\href{https://arxiv.org/abs/2103.09603}{2103.09603} [stat.ML]}
}
```
## Acknowledgements
Funding by the Deutsche Forschungsgemeinschaft (DFG, German Research
Foundation) is acknowledged – Project Number 431701914.
## References
* Bach, P., Chernozhukov, V., Kurz, M. S., Spindler, M. and Klaassen, S. (2024), DoubleML - An Object-Oriented Implementation of Double Machine Learning in R, Journal of Statistical Software, 108(3): 1-56, <doi:10.18637/jss.v108.i03>, arXiv:[2103.09603](https://arxiv.org/abs/2103.09603).
* 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, <https://doi.org/10.1111/ectj.12097>.
* Lang, M., Binder, M., Richter, J., Schratz, P., Pfisterer, F., Coors, S., Au, Q., Casalicchio, G., Kotthoff, L., Bischl, B. (2019), mlr3: A modern object-oriented machine learing framework in R. Journal of Open Source Software, <https://doi.org/10.21105/joss.01903>.