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The Error Term and its Interpretation in Structural Models in Econometrics

In Phyllis McKay Illari Federica Russo (ed.), Causality in the Sciences. Oxford University Press (2011)

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  1. Correlational Data, Causal Hypotheses, and Validity.Federica Russo - 2011 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 42 (1):85 - 107.
    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 (...)
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  • Functions and Mechanisms in Structural-Modelling Explanations.Guillaume Wunsch, Michel Mouchart & Federica Russo - 2014 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 45 (1):187-208.
    One way social scientists explain phenomena is by building structural models. These models are explanatory insofar as they manage to perform a recursive decomposition on an initial multivariate probability distribution, which can be interpreted as a mechanism. Explanations in social sciences share important aspects that have been highlighted in the mechanisms literature. Notably, spelling out the functioning the mechanism gives it explanatory power. Thus social scientists should choose the variables to include in the model on the basis of their function (...)
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