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In the current ML field models are getting larger and more complex, and dataused for model training are also getting larger in quantity and higher indimensions. Therefore, in order to train better models, and save training timeand computational resources, a good Feature Selection (FS) method in thepreprocessing stage is necessary. Feature importance (FI) is of greatimportance since it is the basis of feature selection. Therefore, this papercreatively introduces the calculation of PN (the probability of Necessity), PN(the probability of Sufficiency), and PNS (the probability of Necessity andSufficiency) of Causality into quantifying feature importance and creates 3 newFI measuring methods, PN-FI, which means how much importance a feature has inimage recognition tasks, PS-FI that means how much importance a feature has inimage generating tasks, and PNS-FI which measures both. The main body of thispaper is three RCTs, with whose results we show how PS-FI, PN-FI, and PNS-FI of3 features, dog nose, dog eyes, and dog mouth are calculated. The experimentsshow that firstly, FI values are intervals with tight upper and lower bounds.Secondly, the feature dog eyes has the most importance while the other two havealmost the same. Thirdly, the bounds of PNS and PN are tighter than the boundsof PS.

Causality-Based Feature Importance Quantifying Methods: PN-FI, PS-FI and PNS-FI