Causal and anti-causal learning in pattern recognition for neuroimaging

By Sebastian Weichwald, Bernhard Schölkopf, Tonio Ball, Moritz Grosse-Wentrup. 2015

15 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Medical Imaging, Causal Inference & Discovery, Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Nan Rosemary Ke
2. Anirudh Goyal
3. Stefan Bauer
4. Yoshua Bengio
5. Bernhard Schölkopf
6. Niki Kilbertus
7. Nal Kalchbrenner
8. Giambattista Parascandolo

In Medical Imaging

1. Bernhard Schölkopf

In Causal Inference & Discovery

1. Yoshua Bengio
2. Anirudh Goyal
3. Stefan Bauer
4. Francesco Locatello
5. Nan Rosemary Ke
6. Nal Kalchbrenner
7. Bernhard Schölkopf
8. Giambattista Parascandolo

In Neuroscience

1. Bernhard Schölkopf
2. Yoshua Bengio
3. Anirudh Goyal

Citations in the corpus

• Toward Causal Representation Learning (2021, background): “It also led to a range of follow-up work [32], [78], [97], [114], [115], [152], [153], [156], [167], [195], [204], [243], [263], [267], [277], [278], [281], complementing the studies of Bareinboim and Pearl [14], [185], and it inspired a thread of work in the statistics community exploiting…”
• Causality for Machine Learning (2019, background): “The line of work provided rather fruitful (Zhang et al., 2013; Weichwald et al., 2014; Zhang et al., 2015; Gong et al., 2016; Huang et al., 2017; Blöbaum et al., 2016; Guo et al., 2018; Wang et al., 2019; Subbaswamy et al., 2018; Rabanser et al., 2018; Li et al., 2018a; Lipton et al., 2018; Li et al”
• Generalization in anti-causal learning (2018, background): “Recent work argues that decoders and encoders correspond to causal and anti-causal mappings respectively (Weichwald et al., 2014; Besserve et al., 2018).”