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(.org)](https://app.travis-ci.com/kimmo1019/CausalEGM.svg?branch=main)](https://app.travis-ci.com/github/kimmo1019/CausalEGM)\n[![All Platforms](https://dev.azure.com/conda-forge/feedstock-builds/_apis/build/status/causalegm-feedstock?branchName=main)](https://dev.azure.com/conda-forge/feedstock-builds/_build/latest?definitionId=18625\u0026branchName=main)\n[![Documentation Status](https://readthedocs.org/projects/causalegm/badge/?version=latest)](https://causalegm.readthedocs.io)\n\n\n# \u003ca href='https://causalegm.readthedocs.io/'\u003e\u003cimg src='https://raw.githubusercontent.com/SUwonglab/CausalEGM/main/docs/source/logo.png' align=\"left\" height=\"60\" /\u003e\u003c/a\u003e An Encoding Generative Modeling Approach for Dimension Reduction and Covariate Adjustment\n\n\n\u003ca href='https://causalegm.readthedocs.io/'\u003e\u003cimg align=\"left\" src=\"https://github.com/SUwonglab/CausalEGM/blob/main/model.jpg\" width=\"350\"\u003e\n   \nCausalEGM is a general causal inference framework for estimating causal effects by encoding generative modeling, which can be applied in both discrete and continuous treatment settings. \n\nCausalEGM 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.\n\nCausalEGM 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/).\n\nNote 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.\n\n## CausalEGM Main Applications\n\n- Estimate average treatment effect (ATE).\n\n- Estimate individual treatment effect (ITE).\n\n- Estiamte average dose response function (ADRF).\n\n- Estimate conditional average treatment effect (CATE).\n\n- Built-in simulation and semi-simulation datasets.\n\nCheckout 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).\n\n## Latest News\n\n- May/2024: CausalEGM is published online on [PNAS](https://www.pnas.org/doi/abs/10.1073/pnas.2322376121).\n\n- Mar/2023: CausalEGM is available in CRAN as a stand-alone [R package](https://cran.r-project.org/web/packages/RcausalEGM/index.html).\n\n- Feb/2023: Version 0.2.6 of CausalEGM is released on [Anaconda](https://anaconda.org/conda-forge/causalegm).\n\n- Dec/2022: Preprint paper of CausalEGM is out on [arXiv](https://arxiv.org/abs/2212.05925/).\n\n- Aug/2022: Version 0.1.0 of CausalEGM is released on [PyPI](https://pypi.org/project/CausalEGM/).\n\n## Datasets\n\nCreate a `CausalEGM/data` folder and uncompress the dataset in the `CausalEGM/data` folder.\n\n- [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).\n\n- [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).\n  \n\n## Main Reference\n\nIf you find CausalEGM useful for your work, please consider citing our [PNAS paper](https://www.pnas.org/doi/abs/10.1073/pnas.2322376121):\n\nQiao 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.\n\n## Support\n\nFound a bug or would like to see a feature implemented? Feel free to submit an [issue](https://github.com/SUwonglab/CausalEGM/issues/new/choose). \n\nHave 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). \n\nYour help to improve CausalEGM is highly appreciated! For further information visit [https://causalegm.readthedocs.io/](https://causalegm.readthedocs.io/).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsuwonglab%2Fcausalegm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsuwonglab%2Fcausalegm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsuwonglab%2Fcausalegm/lists"}