Causal scientific explanations from machine learning.
Machine learning is used more and more in scientific contexts, from the recent breakthroughs with AlphaFold2 in protein fold prediction to the use of ML in parametrization for large climate/astronomy models. Yet it is unclear whether we can obtain scientific explanations from such models. I argue th...
| Publicado en: | Synthese Vol. 202; no. 6; pp. 1 - 17 |
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| Formato: | Artículo |
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Springer Nature
Dec2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=174170429&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 174170429 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Dec2023 vid: 202 iid: 6 pid: 237 pub: Springer Nature artinfo: ui: 174170429 10.1007/s11229-023-04429-3 ppf: 1 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P size: 438KB tig: atl: Causal scientific explanations from machine learning. aug: au: Buijsman, Stefan affil: https://ror.org/02e2c7k09 TU Delft, Jaffalaan 5, 2628 BX, Delft, The Netherlands sug: keyword: Artificial intelligence Causal inference Machine learning Scientific explanation ab: Machine learning is used more and more in scientific contexts, from the recent breakthroughs with AlphaFold2 in protein fold prediction to the use of ML in parametrization for large climate/astronomy models. Yet it is unclear whether we can obtain scientific explanations from such models. I argue that when machine learning is used to conduct causal inference we can give a new positive answer to this question. However, these ML models are purpose-built models and there are technical results showing that standard machine learning models cannot be used for the same type of causal inference. Instead, there is a pathway to causal explanations from predictive ML models through new explainability techniques; specifically, new methods to extract structural equation models from such ML models. The extracted models are likely to suffer from issues though: they will often fail to account for confounders and colliders, as well as deliver simply incorrect causal graphs due to ML models tendency to violate physical laws such as the conservation of energy. In this case, extracted graphs are a starting point for new explanations, but predictive accuracy is no guarantee for good explanations. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2023. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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