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Philosophers and statisticians have been debating on causality for a long time. However, these discussions have been led quite independently from each other. An objective of this paper is to pursue a fruitful dialogue between philosophy and statistics. As is well known, at the beginning of the 20th century, some philosophers and statisticians dismissed the concept of causality altogether. It will suffice to mention Bertrand Russell (1913) and Karl Pearson (1911). Almost a hundred years later, causality still represents a central topic both in philosophy and statistics. In the social sciences, including research on public health, most studies are concerned with the possible causes, determinants, factors, etc. of a set of observations. In particular, for planning or policy reasons, it is important to know what causes which effects. In order to attain causal knowledge, many social scientists appeal to statistical modelling to confirm or disconfirm their hypotheses about possible causal relations among the variables they consider, taking care of controlling for relevant covariates and especially for possible confounding factors. To what extent can a statistical model say something about causal relations among variables? In this paper, we will attempt an answer by examining a special class of statistical models, i.e. structural models. The discussion, however, will not be confined to a mere examination of statistical methods, since a considerable effort will be made to consider causality from an epistemological perspective. To put it otherwise, this paper does not address the nature of causation itself, nor the analysis of various causal structures, nor the elaboration of complex causal structures; rather, we will be concerned with the question of how we come to uncover causal relations by means of statistical modelling. The practice of statistical modelling raises substantial issues of ontological nature..
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