DeepROCK: Error-controlled interaction detection in deep neural networks
Winston Chen William Stafford Noble Yang Young Lu
The complexity of deep neural networks (DNNs) makes them powerful but also
makes them challenging to interpret, hindering their applicability in
error-intolerant domains. Existing methods attempt to reason about the internal
mechanism of DNNs by identifying feature interactions that influence prediction
outcomes. However, such methods typically lack a systematic strategy to
prioritize interactions while controlling confidence levels, making them
difficult to apply in practice for scientific discovery and hypothesis
validation. In this paper, we introduce a method, called DeepROCK, to address
this limitation by using knockoffs, which are dummy variables that are designed
to mimic the dependence structure of a given set of features while being
conditionally independent of the response. Together with a novel DNN
architecture involving a pairwise-coupling layer, DeepROCK jointly controls the
false discovery rate (FDR) and maximizes statistical power. In addition, we
identify a challenge in correctly controlling FDR using off-the-shelf feature
interaction importance measures. DeepROCK overcomes this challenge by proposing
a calibration procedure applied to existing interaction importance measures to
make the FDR under control at a target level. Finally, we validate the
effectiveness of DeepROCK through extensive experiments on simulated and real
datasets.