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Correlational Data, Causal Hypotheses, and Validity

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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.

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Notes

  1. True, there are areas in social science where experimental methods are becoming increasingly popular. Yet, randomised experiments may raise more troubles than the ones they are actually meant to solve. The reason is that special care is needed in setting up experiments in social contexts because the intervention that is supposed to test a putative causal relation may change the structure altogether. This problem in social science is known at least since the so-called Lucas critique (Lucas 1976). For a discussion of structure-altering interventions in social contexts, the interested reader may also look at Steel (Steel 2008a, ch. 8).

  2. The reader interested in more technical and formal aspects of structural modelling, especially related to the condition of exogeneity and to recursive decompositions, may look at Mouchart et al. (2009) and Mouchart and Russo (2011).

  3. A thorough discussion of the assumptions of causal models is given in Russo (2009, ch. 4).

  4. Structural modellers try, as much as possible, to build recursive or acyclic models. In simple terms, this means that, given a graphical representation of the mechanism by means of a set of vertices and of directed edges connecting one vertex to another, we do not run into a loop going through the various possible paths in the graph. As a matter of fact, this assumptions of acyclicity is often violated and in fact much research in methodology is devoted to develop models that cope with this aspect.

  5. Exogeneity is a thorny issue for structural modellers. Intuitively, when we say that a variable X is exogenous for a variable Y, this means that some conditions about the parameters of X and Y are satisfied such that it is legitimate to interpret X as the cause and Y as the effect. It is in this sense that it is commonly said that exogenous variables are generated outside the model, i.e. they are not caused by other variables in the model. For a discussion, see for instance Mouchart and Russo (2011).

  6. Philosophers of science have been long debating on the status of models and on their relation to theory and to background knowledge. It is widely agreed that models are not tested by empirical data alone and that background knowledge and general theories contribute to model-building and model-testing also in scientific domains other than the one discussed here. The interested reader may have a look at Frigg and Hartmann (2009) and at the references therein, in particular Morgan and Morrison (1999).

  7. See for instance Bollen (1989) and Wunsch (2007).

  8. A general discussion of validity in causal analysis in social science is given in Russo (2009, ch. 3). The reader interested in external validity—an emerging topic in the philosophical debate—may also have a look at Guala (2005), Steel (2008a), at their two contributions at the PSA 2008 (Guala 2008 and Steel 2008b) and at Jiménez and Miller (2010).

  9. ‘Explanatory manipulationism’ is a coinage, as far as I know, of Strevens.

  10. The question arises whether we really need the whole manipulationist machinery in cases like this. It may be argued, in fact, that in such cases background knowledge is sufficient to exclude spurious causal relations. For a discussion of the explanatory import of manipulationism, see also Russo (2011).

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Acknowledgments

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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    Federica Russo

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Russo, F. Correlational Data, Causal Hypotheses, and Validity. J Gen Philos Sci 42, 85–107 (2011). https://doi.org/10.1007/s10838-011-9157-x

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