Correlational Data, Causal Hypotheses, and Validity

Author: Russo, Federica

Source: Journal for General Philosophy of Science, Volume 42, Number 1, May 2011 , pp. 85-107(23)

Publisher: Springer

Buy & download fulltext article:

Price: $47.00 plus tax (Refund Policy)

Abstract:

A shared problem across the sciences is to make sense of correlational data coming from observations and/or from experiments. Arguably, this means establishing when correlations are causal and when they are not. This is an old problem in philosophy. This paper, narrowing down the scope to quantitative causal analysis in social science, reformulates the problem in terms of the validity of statistical models. Two strategies to make sense of correlational data are presented: first, a `structural strategy', the goal of which is to model and test causal structures that explain correlational data; second, a `manipulationist or interventionist strategy', that hinges upon the notion of invariance under intervention. It is argued that while the former can offer a solution the latter cannot.

Keywords: Causal hypotheses; Causal modelling; Causation; Correlation; Manipulationism; Intervention; Mechanism; Recursive decomposition; Structural modelling; Validity

Document Type: Research article

DOI: http://dx.doi.org/10.1007/s10838-011-9157-x

Affiliations: 1: Philosophy - SECL, University of Kent, Kent, UK, Email: f.russo@kent.ac.uk

Publication date: 2011-05-01