Correlational Data, Causal Hypotheses, and Validity
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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.
KeywordsCausal hypotheses Causal modelling Causation Correlation Manipulationism Intervention Mechanism Recursive decomposition Structural modelling Validity
I am still unsure whether endorsing the ‘anti-counterfactual Pittsburgh tradition’ has been a cause or an effect of me visiting the Center for Philosophy of Science (Pittsburgh) in Spring 2009. At any rate, I am extremely grateful to Peter Machamer for the stimulating discussions we had in Pittsburgh during my stay. His numerous suggestions and comments led me to completely rewrite an earlier draft of this paper. Very helpful comments also came from Lorenzo Casini, Phyllis McKay Illari, Jon Williamson, two anonymous referees, and the editors. Financial support from the Belgian FRS-FNRS (until September 2009) and from the British Academy (from October 2009) is also gratefully acknowledged.
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