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By A. Reutlinger

This primary complete size therapy of interventionist theories of causation within the social sciences, the organic sciences and different higher-level sciences the provides unique counter arguments to contemporary developments within the debate and serves as invaluable advent to the topic.

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Therefore, let us explore some more detailed information on Bayesian networks that is often implicitly used by proponents of interventionist theories of causation. A Bayesian network6 is an ordered pair 〈G, Pr〉, with a directed acyclic graph (DAG) G consisting of and defined for: • a set of variables V; and • a set of directed edges connecting the variables, and a joint probability distribution Pr over the variables of the graph. e. variables and edges), and a probability distribution over a causal graph.

Interventionists respect and, prima facie, satisfy this requirement as a criterion of adequacy: according to the interventionist view, causation requires invariant relationships between cause and effect. These invariant relations are described by so-called invariant generalizations, which are not required to be universal. However, the invariance of a generalization is defined in terms of possible interventions; that is, a generalization is invariant under possible interventions. This is unfortunate for interventionists, because the notion of a possible intervention is a troubled one, as Causation in the Special Sciences 23 argued in Chapter 4.

According to Woodward and Hitchcock (2003: 17) and Woodward (2003: 250), a generalization G is minimally invariant iff the testing intervention condition holds for G. The testing intervention condition states for a generalization G of the form Y = f(X): (a) there are at least two different possible values of an endogenous variable X, x1 and x2, for which Y realizes a value in the way that G describes, and (b) the fact that X takes x1 or, alternatively, x2 is the result of an intervention. The invariance theory of laws will be the subject of Chapter 5.

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