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docs/source/index.rst

@@ -21,24 +21,30 @@ CausalEGM provides a flexible and powerful framework to develop deep learning-ba
 CausalEGM Wide Applicability
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 - Estimate counterfactual outcome.
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 - Estimate average treatment effect (ATE).
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 - Estimate individual treatment effect (ITE).
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 - Estiamte average dose response function (ADRF).
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 - Estimate conditional average treatment effect (CATE).
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 - Built-in simulation and semi-simulation datasets.
 
 CausalEGM Highlighted Features
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 - Capable of handling both continuous and binary treatment settings.
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 - Support big dataset with large sample size (e.g, >10M) and number of covariates (e.g., >10k) in a personal PC.
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 - Provide both `Python PyPi package <https://pypi.org/project/CausalEGM/>`__ and `R CRAN package <https://cran.r-project.org/web/packages/RcausalEGM/index.html>`__, incluidng a user-friendly command-line interface (CLI).
 
 
 Main References
 ^^^^^^^^^^^^^^^
 Liu *et al.* (2022), CausalEGM: a general causal inference framework by encoding generative modeling,
 `arXiv <https://arxiv.org/abs/2212.05925>`__.
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 Liu *et al.* (2021), Density estimation using deep generative neural networks, `PNAS <https://www.pnas.org/doi/abs/10.1073/pnas.2101344118>`_.