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CausalEGM - A general causal inference framework by encoding generative modeling

CausalEGM is a general causal inference framework Liu et al. (arXiv, 2022) for estimating causal effects by encoding generative modeling, which can be applied in both binary 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’s key applications

  • Estimate average treatment effect (ATE).

  • Estimate individual treatment effect (ITE).

  • Estiamte average dose response function (ADRF).

  • Estimate heterogenous treatment effect (HTE).

  • Built-in simulation and semi-simulation datasets.

Latest news

  • Mar/2023: CausalEGM is released 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.

Main References

Liu et al. (2021), Density estimation using deep generative neural networks, PNAS.

Liu et al. (2022), CausalEGM: a general causal inference framework by encoding generative modeling, arXiv.


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