David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
Learn more about PhilPapers
This paper deals with causal analysis in the social sciences. We first present a conceptual framework according to which causal analysis is based on a rationale of variation and invariance, and not only on regularity. We then develop a formal framework for causal analysis by means of structural modelling. Within this framework we approach causality in terms of exogeneity in a structural conditional model based which is based on (i) congruence with background knowledge, (ii) invariance under a large variety of environmental changes, and (iii) model fit. We also tackle the issue of confounding and show how latent confounders can play havoc with exogeneity. This framework avoids making untestable metaphysical claims about causal relations and yet remains useful for cognitive and action-oriented goals
|Keywords||No keywords specified (fix it)|
No categories specified
(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
Joseph Y. Halpern & Judea Pearl (2005). Causes and Explanations: A Structural-Model Approach. Part I: Causes. British Journal for the Philosophy of Science 56 (4):843-887.
R. K. Tavakol (1991). Fragility and Deterministic Modelling in the Exact Sciences. British Journal for the Philosophy of Science 42 (2):147-156.
D. Steel (2012). Federica Russo * Causality and Causal Modelling in the Social Sciences: Measuring Variations. British Journal for the Philosophy of Science 63 (3):725-728.
Federica Russo & Jon Williamson (2007). Interpreting Causality in the Health Sciences. International Studies in the Philosophy of Science 21 (2):157 – 170.
Judea Pearl (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.
Peter Spirtes, Thomas Richardson, Chris Meek & Richard Scheines, Using Path Diagrams as a Structural Equation Modelling Tool.
François Claveau (2011). Evidential Variety as a Source of Credibility for Causal Inference: Beyond Sharp Designs and Structural Models. Journal of Economic Methodology 18 (3):233-253.
Added to index2009-01-28
Total downloads564 ( #1,452 of 1,793,162 )
Recent downloads (6 months)204 ( #739 of 1,793,162 )
How can I increase my downloads?