{"title":"因果关系模型","authors":"Richard Corry","doi":"10.1093/oso/9780198840718.003.0009","DOIUrl":null,"url":null,"abstract":"This chapter shows how the ontology of power and influence can be used to interpret and extend the causal modelling framework developed by Judea Pearl, Peter Spirtes, Clark Glymour, and Richard Scheines. In particular, it is argued that the standard causal modelling framework suffers from an important limitation in that it is not truly modular. A modification to the standard framework is presented that overcomes this limitation. In the modified framework, the basic relations explicitly represent basic causal powers and the influences that they manifest. These ‘causal influence models’ can be used to generate standard causal models, and so can do everything that the standard causal models can do. It is argued, however, that there are both theoretical and practical reasons for preferring causal influence models over standard causal models.","PeriodicalId":173983,"journal":{"name":"Power and Influence","volume":"289 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"48","resultStr":"{\"title\":\"Causal Models\",\"authors\":\"Richard Corry\",\"doi\":\"10.1093/oso/9780198840718.003.0009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This chapter shows how the ontology of power and influence can be used to interpret and extend the causal modelling framework developed by Judea Pearl, Peter Spirtes, Clark Glymour, and Richard Scheines. In particular, it is argued that the standard causal modelling framework suffers from an important limitation in that it is not truly modular. A modification to the standard framework is presented that overcomes this limitation. In the modified framework, the basic relations explicitly represent basic causal powers and the influences that they manifest. These ‘causal influence models’ can be used to generate standard causal models, and so can do everything that the standard causal models can do. It is argued, however, that there are both theoretical and practical reasons for preferring causal influence models over standard causal models.\",\"PeriodicalId\":173983,\"journal\":{\"name\":\"Power and Influence\",\"volume\":\"289 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"48\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Power and Influence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/oso/9780198840718.003.0009\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Power and Influence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/oso/9780198840718.003.0009","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This chapter shows how the ontology of power and influence can be used to interpret and extend the causal modelling framework developed by Judea Pearl, Peter Spirtes, Clark Glymour, and Richard Scheines. In particular, it is argued that the standard causal modelling framework suffers from an important limitation in that it is not truly modular. A modification to the standard framework is presented that overcomes this limitation. In the modified framework, the basic relations explicitly represent basic causal powers and the influences that they manifest. These ‘causal influence models’ can be used to generate standard causal models, and so can do everything that the standard causal models can do. It is argued, however, that there are both theoretical and practical reasons for preferring causal influence models over standard causal models.