An Encoding Generative Modeling Approach for Dimension Reduction and Covariate Adjustment

CausalEGM 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.

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 and R 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.

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 and R Tutorial.

Latest News

• May/2024: CausalEGM is published online on PNAS.

• Mar/2023: CausalEGM is available in CRAN as a stand-alone R package.

• Feb/2023: Version 0.2.6 of CausalEGM is released on Anaconda.

• Dec/2022: Preprint paper of CausalEGM is out on arXiv.

• Aug/2022: Version 0.1.0 of CausalEGM is released on PyPI.

Datasets

Create a CausalEGM/data folder and uncompress the dataset in the CausalEGM/data folder.

• Twin dataset. Google Drive download link.

• ACIC 2018 datasets. Google Drive download link.

Main Reference

If you find CausalEGM useful for your work, please consider citing our PNAS paper:

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.

Have a question or would like to start a new discussion? You can also always send us an e-mail.

Your help to improve CausalEGM is highly appreciated! For further information visit https://causalegm.readthedocs.io/.