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While probabilistic models describe the dependence structure between observedvariables, causal models go one step further: they predict, for example, howcognitive functions are affected by external interventions that perturbneuronal activity. In this review and perspective article, we introduce theconcept of causality in the context of cognitive neuroscience and reviewexisting methods for inferring causal relationships from data. Causal inferenceis an ambitious task that is particularly challenging in cognitiveneuroscience. We discuss two difficulties in more detail: the scarcity ofinterventional data and the challenge of finding the right variables. We arguefor distributional robustness as a guiding principle to tackle these problems.Robustness (or invariance) is a fundamental principle underlying causalmethodology. A causal model of a target variable generalises acrossenvironments or subjects as long as these environments leave the causalmechanisms intact. Consequently, if a candidate model does not generalise, theneither it does not consist of the target variable's causes or the underlyingvariables do not represent the correct granularity of the problem. In thissense, assessing generalisability may be useful when defining relevantvariables and can be used to partially compensate for the lack ofinterventional data.

Causality in cognitive neuroscience: concepts, challenges, and distributional robustness