Identifying Intended Effects with Causal Models
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909107007520768 |
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| author | Compagno, Dario |
| author_facet | Compagno, Dario |
| contents | The aim of this paper is to extend the framework of causal inference, in particular as it has been developed by Judea Pearl, in order to model actions and identify their intended effects, in the direction opened by Elisabeth Anscombe. We show how intentions can be inferred from a causal model and its implied correlations observable in data. The paper defines confounding effects as the reasons why teleological inference may fail and introduces interference as a way to control for them. The ''fundamental problem'' of teleological inference is presented, explaining why causal analysis needs an extension in order to take intentions into account. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09472 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identifying Intended Effects with Causal Models Compagno, Dario Methodology Data Analysis, Statistics and Probability The aim of this paper is to extend the framework of causal inference, in particular as it has been developed by Judea Pearl, in order to model actions and identify their intended effects, in the direction opened by Elisabeth Anscombe. We show how intentions can be inferred from a causal model and its implied correlations observable in data. The paper defines confounding effects as the reasons why teleological inference may fail and introduces interference as a way to control for them. The ''fundamental problem'' of teleological inference is presented, explaining why causal analysis needs an extension in order to take intentions into account. |
| title | Identifying Intended Effects with Causal Models |
| topic | Methodology Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2402.09472 |