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A deep learning (DL) model learns a function relating a set of input variables with a set of target variables. While the representation of this function in form of the DL model often lacks interpretability, several interpretation methods exist that provide descriptions of the function (e.g., measures of feature importance). On the one hand, these descriptions may build trust in the model or reveal its limitations. On the other hand, they may lead to new scientific understanding. In any case, a description is only useful if one is able to identify if parts of it reflect spurious instead of causal relations (e.g., random associations in the training data instead of associations due to a physical process). However, this can be challenging even for experts because, in scientific tasks, causal relations between input and target variables are often unknown or extremely complex. Commonly, this challenge is addressed by training separate instances of the considered model on random samples of the training set and identifying differences between the obtained descriptions. Here, we demonstrate that this may not be sufficient and propose to additionally consider more general modifications of the prediction task. We refer to the proposed approach as variant approach and demonstrate its usefulness and its superiority over pure sampling approaches with two illustrative prediction tasks from hydrometeorology. While being conceptually simple, to our knowledge the approach has not been formalized and systematically evaluated before.

Variant Approach for Identifying Spurious Relations That Deep Learning Models Learn