PhilSci Archive

Correlational data, causal hypotheses, and validity

Russo, Federica (2010) Correlational data, causal hypotheses, and validity. [Preprint]


Download (357kB)


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.

Export/Citation: EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL
Social Networking:
Share |

Item Type: Preprint
Keywords: Causal hypotheses; Causal modelling; Causation; Correlation; Manipulationism; Intervention; Mechanism; Recursive decomposition; Structural modelling; Validity.
Subjects: General Issues > Causation
Depositing User: Dr Federica Russo
Date Deposited: 26 Oct 2010 15:45
Last Modified: 26 Oct 2010 15:45
Item ID: 8349
Subjects: General Issues > Causation
Date: 25 October 2010

Monthly Views for the past 3 years

Monthly Downloads for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item