Explainable anomaly detection There are plenty of working for explaining prediction in supervised learning (e.g. SHAP values, LIME).

What about for anomaly detection in unsupervised learning? Is there any model for which there are libraries that can give you justifications, such as "row x is an anomaly because feature 1 is higher than 5.3 and feature 5 is equal to 'No'"?
The LIME framework can probably be used to do this as well.

Outlier detection sets a specific label to outliers (say 1), and another one to inliers (say 0). From then on, you can train interpretable models (decision trees for instance) to predict the labels set by your unsupervised model.

I don't know much about SHAP values, but I guess, with this approach, you could do the same.