In recent years there has been a lot of focus on adversarial attacks,
especially on deep neural networks. Here, we argue that they are more general
in nature and can easily affect a larger class of models, e.g., any
differentiable perturbed optimizers. We further show that such attacks can be
determined by the hidden confounders in a domain, thus drawing a novel
connection between such attacks and causality. Establishing this causal
perspective is characterized by the influence of the structural causal model’s
data generating process on the subsequent optimization thereby exhibiting
intriguing parameters of the former. We reveal the existence of such parameters
for three combinatorial optimization problems, namely linear assignment,
shortest path and a real world problem of energy systems. Our empirical
examination also unveils worrisome consequences of these attacks on
differentiable perturbed optimizers thereby highlighting the criticality of our

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