# Causal and anti-causal learning in pattern recognition for neuroimaging

arXiv: https://arxiv.org/abs/1512.04808 · HTML: https://mlsanity.com/paper/1512.04808/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2015.

## Built on by (most cited first, 4 of 4)

- [Towards Causal Representation Learning](https://mlsanity.com/paper/2102.11107/) (2021)
- [Desiderata for Representation Learning: A Causal Perspective](https://mlsanity.com/paper/2109.03795/) (2021)
- [Structural Causal 3D Reconstruction](https://mlsanity.com/paper/2207.10156/) (2022)
- [Causal vs. Anticausal merging of predictors](https://mlsanity.com/paper/2501.08426/) (2025)

## Abstract (arXiv)

Pattern recognition in neuroimaging distinguishes between two types of models: encoding- and decoding models. This distinction is based on the insight that brain state features, that are found to be relevant in an experimental paradigm, carry a different meaning in encoding- than in decoding models. In this paper, we argue that this distinction is not sufficient: Relevant features in encoding- and decoding models carry a different meaning depending on whether they represent causal- or anti-causal relations. We provide a theoretical justification for this argument and conclude that causal inference is essential for interpretation in neuroimaging.