Imprecise Bayesian Networks as Causal Models
This article considers the extent to which Bayesian networks with imprecise probabilities, which are used in statistics and computer science for predictive purposes, can be used to represent causal structure. It is argued that the adequacy conditions for causal representation in the precise context&...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-08-01
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| Series: | Information |
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| Online Access: | http://www.mdpi.com/2078-2489/9/9/211 |
| _version_ | 1818850838875996160 |
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| author | David Kinney |
| author_facet | David Kinney |
| author_sort | David Kinney |
| collection | DOAJ |
| description | This article considers the extent to which Bayesian networks with imprecise probabilities, which are used in statistics and computer science for predictive purposes, can be used to represent causal structure. It is argued that the adequacy conditions for causal representation in the precise context—the Causal Markov Condition and Minimality—do not readily translate into the imprecise context. Crucial to this argument is the fact that the independence relation between random variables can be understood in several different ways when the joint probability distribution over those variables is imprecise, none of which provides a compelling basis for the causal interpretation of imprecise Bayes nets. I conclude that there are serious limits to the use of imprecise Bayesian networks to represent causal structure. |
| first_indexed | 2024-12-19T06:55:30Z |
| format | Article |
| id | doaj.art-eaedbdd4738648ef9199b78b696f4bc7 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-12-19T06:55:30Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-eaedbdd4738648ef9199b78b696f4bc72022-12-21T20:31:32ZengMDPI AGInformation2078-24892018-08-019921110.3390/info9090211info9090211Imprecise Bayesian Networks as Causal ModelsDavid Kinney0Department of Philosophy, Logic and Scientific Method, London School of Economics, London WC2A 2AE, UKThis article considers the extent to which Bayesian networks with imprecise probabilities, which are used in statistics and computer science for predictive purposes, can be used to represent causal structure. It is argued that the adequacy conditions for causal representation in the precise context—the Causal Markov Condition and Minimality—do not readily translate into the imprecise context. Crucial to this argument is the fact that the independence relation between random variables can be understood in several different ways when the joint probability distribution over those variables is imprecise, none of which provides a compelling basis for the causal interpretation of imprecise Bayes nets. I conclude that there are serious limits to the use of imprecise Bayesian networks to represent causal structure.http://www.mdpi.com/2078-2489/9/9/211imprecise probabilitiesBayes netscausal modellingindependence |
| spellingShingle | David Kinney Imprecise Bayesian Networks as Causal Models Information imprecise probabilities Bayes nets causal modelling independence |
| title | Imprecise Bayesian Networks as Causal Models |
| title_full | Imprecise Bayesian Networks as Causal Models |
| title_fullStr | Imprecise Bayesian Networks as Causal Models |
| title_full_unstemmed | Imprecise Bayesian Networks as Causal Models |
| title_short | Imprecise Bayesian Networks as Causal Models |
| title_sort | imprecise bayesian networks as causal models |
| topic | imprecise probabilities Bayes nets causal modelling independence |
| url | http://www.mdpi.com/2078-2489/9/9/211 |
| work_keys_str_mv | AT davidkinney imprecisebayesiannetworksascausalmodels |