  Feature attribution XAI algorithms enable their users to gain insight into
the underlying patterns of large datasets through their feature importance
calculation. Existing feature attribution algorithms treat all features in a
dataset homogeneously, which may lead to misinterpretation of consequences of
changing feature values. In this work, we consider partitioning features into
controllable and uncontrollable parts and propose the Controllable fActor
Feature Attribution (CAFA) approach to compute the relative importance of
controllable features. We carried out experiments applying CAFA to two existing
datasets and our own COVID-19 non-pharmaceutical control measures dataset.
Experimental results show that with CAFA, we are able to exclude influences
from uncontrollable features in our explanation while keeping the full dataset
for prediction.
