  While probabilistic models describe the dependence structure between observed
variables, causal models go one step further: they predict, for example, how
cognitive functions are affected by external interventions that perturb
neuronal activity. In this review and perspective article, we introduce the
concept of causality in the context of cognitive neuroscience and review
existing methods for inferring causal relationships from data. Causal inference
is an ambitious task that is particularly challenging in cognitive
neuroscience. We discuss two difficulties in more detail: the scarcity of
interventional data and the challenge of finding the right variables. We argue
for distributional robustness as a guiding principle to tackle these problems.
Robustness (or invariance) is a fundamental principle underlying causal
methodology. A causal model of a target variable generalises across
environments or subjects as long as these environments leave the causal
mechanisms intact. Consequently, if a candidate model does not generalise, then
either it does not consist of the target variable's causes or the underlying
variables do not represent the correct granularity of the problem. In this
sense, assessing generalisability may be useful when defining relevant
variables and can be used to partially compensate for the lack of
interventional data.
