https://github.com/suwonglab/causalegm
A General Causal Inference Framework by Encoding Generative Modeling
https://github.com/suwonglab/causalegm
causal-inference causal-machine-learning causality deep-generative-model generative-model treatment-effects
Last synced: 7 months ago
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A General Causal Inference Framework by Encoding Generative Modeling
- Host: GitHub
- URL: https://github.com/suwonglab/causalegm
- Owner: SUwonglab
- Created: 2022-06-07T00:44:57.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2024-06-01T23:58:42.000Z (about 2 years ago)
- Last Synced: 2025-10-22T03:48:06.749Z (7 months ago)
- Topics: causal-inference, causal-machine-learning, causality, deep-generative-model, generative-model, treatment-effects
- Language: Python
- Homepage: https://causalegm.readthedocs.io/
- Size: 1.86 MB
- Stars: 71
- Watchers: 3
- Forks: 11
- Open Issues: 1
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
[](https://pypi.org/project/CausalEGM/)
[](https://cran.r-project.org/web/packages/RcausalEGM/index.html)
[](https://anaconda.org/conda-forge/causalegm)
[](https://app.travis-ci.com/github/kimmo1019/CausalEGM)
[](https://dev.azure.com/conda-forge/feedstock-builds/_build/latest?definitionId=18625&branchName=main)
[](https://causalegm.readthedocs.io)
# 
An Encoding Generative Modeling Approach for Dimension Reduction and Covariate Adjustment
CausalEGM simultaneously decouples the dependencies of confounders on both treatment and outcome and maps the confounders to the low-dimensional latent space. By conditioning on the low-dimensional latent features, CausalEGM can estimate the causal effect for each individual or the average causal effect within a population.
CausalEGM was originally developed with Python and TensorFlow. Now both [Python](https://pypi.org/project/CausalEGM/) and [R](https://cran.r-project.org/web/packages/RcausalEGM/index.html) package for CausalEGM are available! Besides, we provide a console program to run CausalEGM directly without running any script. For more information, checkout the [Document](https://causalegm.readthedocs.io/).
Note that a GPU is recommended for accelerating the model training. However, GPU is not a must, CausalEGM can be installed on any personal computer (e.g, Macbook) or computational cluster with CPU only.
## CausalEGM Main Applications
- Estimate average treatment effect (ATE).
- Estimate individual treatment effect (ITE).
- Estiamte average dose response function (ADRF).
- Estimate conditional average treatment effect (CATE).
- Built-in simulation and semi-simulation datasets.
Checkout application examples in the [Python Tutorial](https://causalegm.readthedocs.io/en/latest/tutorial_py.html) and [R Tutorial](https://causalegm.readthedocs.io/en/latest/tutorial_r.html).
## Latest News
- May/2024: CausalEGM is published online on [PNAS](https://www.pnas.org/doi/abs/10.1073/pnas.2322376121).
- Mar/2023: CausalEGM is available in CRAN as a stand-alone [R package](https://cran.r-project.org/web/packages/RcausalEGM/index.html).
- Feb/2023: Version 0.2.6 of CausalEGM is released on [Anaconda](https://anaconda.org/conda-forge/causalegm).
- Dec/2022: Preprint paper of CausalEGM is out on [arXiv](https://arxiv.org/abs/2212.05925/).
- Aug/2022: Version 0.1.0 of CausalEGM is released on [PyPI](https://pypi.org/project/CausalEGM/).
## Datasets
Create a `CausalEGM/data` folder and uncompress the dataset in the `CausalEGM/data` folder.
- [Twin dataset](https://www.nber.org/research/data/linked-birthinfant-death-cohort-data). Google Drive download [link](https://drive.google.com/file/d/1fKCb-SHNKLsx17fezaHrR2j29T3uD0C2/view?usp=sharing).
- [ACIC 2018 datasets](https://www.synapse.org/#!Synapse:syn11294478/wiki/494269). Google Drive download [link](https://drive.google.com/file/d/1qsYTP8NGh82nFNr736xrMsJxP73gN9OG/view?usp=sharing).
## Main Reference
If you find CausalEGM useful for your work, please consider citing our [PNAS paper](https://www.pnas.org/doi/abs/10.1073/pnas.2322376121):
Qiao Liu, Zhongren Chen, Wing Hung Wong. An encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies [J]. PNAS, 2024.
## Support
Found a bug or would like to see a feature implemented? Feel free to submit an [issue](https://github.com/SUwonglab/CausalEGM/issues/new/choose).
Have a question or would like to start a new discussion? You can also always send us an [e-mail](mailto:liuqiao@stanford.edu?subject=[GitHub]%20CausalEGM%20project).
Your help to improve CausalEGM is highly appreciated! For further information visit [https://causalegm.readthedocs.io/](https://causalegm.readthedocs.io/).
