CausalBGM is an AI-powered Bayesian generative modeling approach that captures the causal relationship among covariates, treatment, and outcome variables.
CausalBGM adopts a Bayesian iterative approach to update the model parameters and the posterior distribution of latent features until convergence. This framework leverages the power of AI to capture complex dependencies among variables while adhering to the Bayesian principles.
CausalBGM was developed with Python and TensorFlow. Now both Python and R package for CausalBGM are available! Besides, we provide a console program to run CausalBGM directly. For more information, checkout the Document.
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Point estimate of ATE, ITE, ADRF, CATE.
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Estimate prediction intervals of ATE, ITE, ADRF, CATE with user-specific significant level.
Checkout application examples in the Python Tutorial and R Tutorial.
- Dec/2022: Preprint paper of CausalBGM is out on arXiv.
Create a CausalBGM/data folder and uncompress the dataset in the CausalBGM/data folder.
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Twin dataset. Google Drive download link.
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ACIC 2018 datasets. Google Drive download link.
If you find CausalBGM useful for your work, please consider citing our paper:
Qiao Liu and Wing Hung Wong. An AI-powered Bayesian generative modeling approach for causal inference in observational studies [J]. arXiv preprint arXiv:2212.05925, 2022.
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 CausalBGM is highly appreciated! For further information visit https://causalegm.readthedocs.io/.