Add Conditional Feature Removal (Tabular)

[mmschlk]

Next to interventional approaches also observational explanations should be created with shapiq. For this conditional modelling and sampling of and from the tabular data distributions is required. This arfpy package should do the job.

Tasks

• [ ] Implement Conditional Feature Removal (imputer) for tabular data: Potenitally with arfpy.
• [ ] Make this technique available for the tabular explainer
• [ ] Create tests for the conditional imputer and explainer variants
• [ ] Add documentation to the imputer with the correct links and citations
• [ ] Add Jupyter notebook example that are visible in the documentation (visibility can be handled independently in #38)

[hbaniecki]

look at https://link.springer.com/article/10.1007/s10618-024-01016-z