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@@ -41,7 +41,7 @@ Please cite [our survey paper](https://arxiv.org/pdf/1809.09337) if this index i
 |Uber CausalML|[Chen, Huigang, et al. "Causalml: Python package for causal machine learning." arXiv preprint arXiv:2002.11631 (2020).](https://arxiv.org/pdf/2002.11631.pdf)|[Python](https://github.com/uber/causalml)|
 |JustCause|For evaluation of heterogeneous treatment effect estimators on common reference as well as synthetic data. [Underlying thesis](https://justcause.readthedocs.io/en/latest/_downloads/e054f7a0fc9cf9e680173600cb4b4350/thesis-mfranz.pdf)|[Python](https://github.com/inovex/justcause)|
 |WhyNot|An experimental sandbox for causal inference and decision making in dynamics. [Documentation](https://whynot.readthedocs.io/en/latest/)|[Python](https://github.com/zykls/whynot)|
-|scikit-uplift|Uplift modeling in scikit-learn style in python|[Code](https://github.com/maks-sh/scikit-uplift)|
+|scikit-uplift|Uplift modeling in scikit-learn style in python. [Documentation](https://www.uplift-modeling.com/en/latest/index.html) and [User guide for uplift modeling](https://www.uplift-modeling.com/en/latest/user_guide/index.html)|[Python](https://github.com/maks-sh/scikit-uplift)|
 
 ### Causal Discovery
 |Name|Paper|Code|