Update pfi.rst

docs/source/_model_agnostic_xai/pfi.rst

@@ -3,6 +3,7 @@ Introduction to Permutation Feature Importance
 
 Permutation Feature Importance is a **model-agnostic** method, which means that it is not restricted to a certain model type, 
 and it is a **global** method, which means that it only provides explanations for a full dataset, but not for individual samples.
+Permutation Feature Importance was introduced in 2001 by `Breiman et al. <https://link.springer.com/article/10.1023/a:1010933404324>`_ for Random Forest models and extended to a model-agnostic version in 2018 by `Fischer et al. <https://www.jmlr.org/papers/v20/18-760.html>`_
 
 For a short video introduction to Permutation Feature Importance, click below:
 
@@ -28,4 +29,6 @@ On the other hand, if the performance of the data with the permuted feature is m
 References
 -----------
 
-Molnar, Christoph. `Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. <https://christophm.github.io/interpretable-ml-book/>`_ Lulu.com. 2022.
+- Permutation Feature Importance for Random Forests: Breiman, L. `Random forests. <https://link.springer.com/article/10.1023/a:1010933404324>`_ Machine learning. 2001.
+- Model Agnostic Permutation Feature Importance: Fisher, A., Rudin, C., & Dominici, F. `All models are wrong, but many are useful: Learning a variable's importance by studying an entire class of prediction models simultaneously. <https://www.jmlr.org/papers/v20/18-760.html>`_ Journal of Machine Learning Research. 2018.
+- XAI Book: Molnar, Christoph. `Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. <https://christophm.github.io/interpretable-ml-book/>`_ Lulu.com. 2022.