  The ability to learn and act in novel situations is still a prerogative of
animate intelligence, as current machine learning methods mostly fail when
moving beyond the standard i.i.d. setting. What is the reason for this
discrepancy? Most machine learning tasks are anti-causal, i.e., we infer causes
(labels) from effects (observations). Typically, in supervised learning we
build systems that try to directly invert causal mechanisms. Instead, in this
paper we argue that strong generalization capabilities crucially hinge on
searching and validating meaningful hypotheses, requiring access to a causal
model. In such a framework, we want to find a cause that leads to the observed
effect. Anti-causal models are used to drive this search, but a causal model is
required for validation. We investigate the fundamental differences between
causal and anti-causal tasks, discuss implications for topics ranging from
adversarial attacks to disentangling factors of variation, and provide
extensive evidence from the literature to substantiate our view. We advocate
for incorporating causal models in supervised learning to shift the paradigm
from inference only, to search and validation.
