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

Ref:
en towardsdatascience.com
cn weixin
SHAP for ML.