Causal interpretation rules for encoding and decoding models in neuroimaging

Abstract

Causal terminology is often introduced in the interpretation of encoding and decoding models trained on neuroimaging data. In this article, we investigate which causal statements are warranted and which ones are not supported by empirical evidence. We argue that the distinction between encoding and decoding models is not sufficient for this purpose: relevant features in encoding and decoding models carry a different meaning in stimulus- and in response-based experimental paradigms. We show that only encoding models in the stimulus-based setting support unambiguous causal interpretations. By combining encoding and decoding models trained on the same data, however, we obtain insights into causal relations beyond those that are implied by each individual model type. We illustrate the empirical relevance of our theoretical findings on EEG data recorded during a visuo-motor learning task.

arXiv commentaccepted manuscript

References in corpus (5)

• Kernel-based Conditional Independence Test and Application in Causal Discovery
• Differentiating information transfer and causal effect
• Strong Completeness and Faithfulness in Bayesian Networks
• On Causal and Anticausal Learning
• Causal discovery of linear acyclic models with arbitrary distributions

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• ECKO: Ensemble of Clustered Knockoffs for multivariate inference on fMRI data
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