ML Interpratation, Comprehensibility & Causality

See also: DL-theory > ## cnn visualization/comprehensibility 可解释的机器学习 (What) 可解释性的重要性 (Why) 具体如何解释 (How) Insights which can be extracted from the models Permutation Importance Partial Dependency Plots SHAP Values SHAP Values in Advance LIME (万金油), Tree interpreter, etc. 凭什么相信你，我的CNN模型？关于CNN模型可解释性的思考 inc.:CAM, Grad-CAM, Lime. Book by Christoph Molnar: Interpretable Machine Learning – A Guide for Making Black Box Models Explainable (GitHub), (CN) All-in-one: SHAP. ref: https://medium.com/@Zelros/a-brief-history-of-machine-learning-models-explainability-f1c3301be9dc Causality / Causal Inference # Jonas Peters, Dominik Janzing and Bernhard Schölkopf 2017 - Elements of Causal Inference ; Bernhard Schölkopf 2019 - Causality for Machine Learning, ...