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,
  via 贝叶斯网络之父Judea Pearl力荐、LeCun点赞，这篇长论文全面解读机器学习中的因果关系 机器学习研究会订阅号
• 图灵奖得主Judea Pearl ：从“贝叶斯网络之父”到“AI社区的叛徒”

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