Causality in Cognitive Neuroscience: Concepts, Challenges, and Distributional Robustness
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Causality in Cognitive Neuroscience : Concepts, Challenges, and Distributional Robustness. / Weichwald, Sebastian; Peters, Jonas.
In: Journal of Cognitive Neuroscience, Vol. 33, No. 2, 02.2021, p. 226-247.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Causality in Cognitive Neuroscience
T2 - Concepts, Challenges, and Distributional Robustness
AU - Weichwald, Sebastian
AU - Peters, Jonas
PY - 2021/2
Y1 - 2021/2
N2 - Whereas 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 (correctly specified) causal model of a target variable generalizes across environments or subjects as long as these environments leave the causal mechanisms of the target intact. Consequently, if a candidate model does not generalize, 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 generalizability may be useful when defining relevant variables and can be used to partially compensate for the lack of interventional data.
AB - Whereas 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 (correctly specified) causal model of a target variable generalizes across environments or subjects as long as these environments leave the causal mechanisms of the target intact. Consequently, if a candidate model does not generalize, 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 generalizability may be useful when defining relevant variables and can be used to partially compensate for the lack of interventional data.
U2 - 10.1162/jocn_a_01623
DO - 10.1162/jocn_a_01623
M3 - Journal article
C2 - 32812827
VL - 33
SP - 226
EP - 247
JO - Journal of Cognitive Neuroscience
JF - Journal of Cognitive Neuroscience
SN - 0898-929X
IS - 2
ER -
ID: 258901390