David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
British Journal for the Philosophy of Science 64 (2):423-448 (2013)
The framework of causal Bayes nets, currently influential in several scientific disciplines, provides a rich formalism to study the connection between causality and probability from an epistemological perspective. This article compares three assumptions in the literature that seem to constrain the connection between causality and probability in the style of Occam's razor. The trio includes two minimality assumptions—one formulated by Spirtes, Glymour, and Scheines (SGS) and the other due to Pearl—and the more well-known faithfulness or stability assumption. In terms of logical strength, it is fairly obvious that the three form a sequence of increasingly stronger assumptions. The focus of this article, however, is to investigate the nature of their relative strength. The comparative analysis reveals an important sense in which Pearl's minimality assumption is as strong as the faithfulness assumption and identifies a useful condition under which it is as safe as SGS's relatively secure minimality assumption. Both findings have notable implications for the theory and practice of causal inference. 1 Introduction2 Background: Inference of Causal Structure in Markovian Causal Models3 Three Assumptions of Simplicity4 A Comparison of P-minimality and Faithfulness5 A Comparison of P-minimality and SGS-minimality6 Methodological Formulations and Prior Knowledge of Causal Order7 Conclusion
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Peter Spirtes (2011). Intervention, Determinism, and the Causal Minimality Condition. Synthese 182 (3):335-347.
Donald Gillies & Aidan Sudbury (2013). Should Causal Models Always Be Markovian? The Case of Multi-Causal Forks in Medicine. European Journal for Philosophy of Science 3 (3):275-308.
Jiji Zhang & Peter Spirtes (2008). Detection of Unfaithfulness and Robust Causal Inference. Minds and Machines 18 (2):239-271.
Peter Spirtes & Richard Scheines (2004). Causal Inference of Ambiguous Manipulations. Philosophy of Science 71 (5):833-845.
Alison Gopnik, Clark Glymour, David M. Sobel, Laura Schulz, Tamar Kushnir & David Danks, A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.
Alison Gopnik, Clark Glymour, David M. Sobel & Laura E. Schultz, Causal Learning in Children: Causal Maps and Bayes Nets.
Richard Scheines, Clark Glymour & Peter Spirtes, Learning the Structure of Linear Latent Variable Models.
Jiji Zhang (2013). A Lewisian Logic of Causal Counterfactuals. Minds and Machines 23 (1):77-93.
Judea Pearl (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.
Added to index2012-10-01
Total downloads13 ( #125,610 of 1,099,863 )
Recent downloads (6 months)6 ( #51,330 of 1,099,863 )
How can I increase my downloads?