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Added Counterfactual Explanations for Machine Learning

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@ -198,6 +198,8 @@ This is an on-going attempt to consolidate interesting efforts in the area of un
* Towards robust interpretability with self-explaining neural networks. _Alvarez-Melis and Jaakola 2018_ [pdf](http://people.csail.mit.edu/tommi/papers/SENN_paper.pdf)
# C. Counterfactual explanations
* Counterfactual Explanations for Machine Learning: A Review. _Verma et al. 2020_ [pdf](https://arxiv.org/pdf/2010.10596.pdf)
* Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections. _Zhang et al. 2018_ [pdf](http://papers.nips.cc/paper/7736-interpreting-neural-network-judgments-via-minimal-stable-and-symbolic-corrections.pdf)
* Counterfactual Visual Explanations. _Goyal et al. 2019_ [pdf](https://arxiv.org/pdf/1904.07451.pdf)
* Generative Counterfactual Introspection for Explainable Deep Learning. _Liu et al. 2019_ [pdf](https://arxiv.org/abs/1907.03077)