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Measuring Causal Invariance Formally
Invariance is one of several dimensions of causal relationships within the interventionist account. The more invariant a relationship between two variables, the more the relationship should be considered paradigmatically causal. In this paper, I propose two formal measures to estimate invariance, il...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8228138/ https://www.ncbi.nlm.nih.gov/pubmed/34070711 http://dx.doi.org/10.3390/e23060690 |
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author | Bourrat, Pierrick |
author_facet | Bourrat, Pierrick |
author_sort | Bourrat, Pierrick |
collection | PubMed |
description | Invariance is one of several dimensions of causal relationships within the interventionist account. The more invariant a relationship between two variables, the more the relationship should be considered paradigmatically causal. In this paper, I propose two formal measures to estimate invariance, illustrated by a simple example. I then discuss the notion of invariance for causal relationships between non-nominal (i.e., ordinal and quantitative) variables, for which Information theory, and hence the formalism proposed here, is not well suited. Finally, I propose how invariance could be qualified for such variables. |
format | Online Article Text |
id | pubmed-8228138 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82281382021-06-26 Measuring Causal Invariance Formally Bourrat, Pierrick Entropy (Basel) Article Invariance is one of several dimensions of causal relationships within the interventionist account. The more invariant a relationship between two variables, the more the relationship should be considered paradigmatically causal. In this paper, I propose two formal measures to estimate invariance, illustrated by a simple example. I then discuss the notion of invariance for causal relationships between non-nominal (i.e., ordinal and quantitative) variables, for which Information theory, and hence the formalism proposed here, is not well suited. Finally, I propose how invariance could be qualified for such variables. MDPI 2021-05-30 /pmc/articles/PMC8228138/ /pubmed/34070711 http://dx.doi.org/10.3390/e23060690 Text en © 2021 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bourrat, Pierrick Measuring Causal Invariance Formally |
title | Measuring Causal Invariance Formally |
title_full | Measuring Causal Invariance Formally |
title_fullStr | Measuring Causal Invariance Formally |
title_full_unstemmed | Measuring Causal Invariance Formally |
title_short | Measuring Causal Invariance Formally |
title_sort | measuring causal invariance formally |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8228138/ https://www.ncbi.nlm.nih.gov/pubmed/34070711 http://dx.doi.org/10.3390/e23060690 |
work_keys_str_mv | AT bourratpierrick measuringcausalinvarianceformally |