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‘Singular Causes First’ rejects Hume's thesis that singular causal facts are reducible to generic ones, adopting a reverse position, taking singular causes as basic. Using idealized examples, Cartwright shows that strategies to establish causal claims without using singular causal facts as inputs all fail, including probabilistic theories of causality. Not only is singular causal input necessary if probabilities are to imply causal connections, the resulting causal output is also at base singular.

Confronts conflicting intuitions about the character of probabilistic causality. These intuitions were exposed with the publication of the preceding essay, and the critics’ responses to it. Using several important additional examples, this essay addresses the responses of I.J. Good to the criticisms raised in the preceding chapter. It exhibits complexities that arise when we try to accommodate sophisticated intuitions about probabilistic causality in an explicitly articulated theory.

This chapter extends the discussion of the preceding chapter, and emphasizes the causal dimensions of explanation in archaeology. The author considers the sorts of situations that archaeologists want to explain, and notes that many of these are events that result from a complex set of factors, some of which are positively relevant to the occurrence of the event and others that are negatively relevant. In addition, many events that archaeologists want to explain are events that had a very low probability of occurrence. The author argues that formal philosophical models of explanation, such as the D‐N and the I‐S models, are ill suited to the task of archaeological explanation. What is needed, he says, is the development of a probabilistic account of causality that can supplement notions of statistical relevance in explanations of archaeological phenomena.

In addition to the notion of causal law, the concept of capacity, modelled on Mill's notion of tendency, is required to make sense of standard methods in econometrics and standard accounts of probabilistic causality. General causal claims are shown to be ascriptions of capacities while causal laws are local causal claims, relative to a test population. Capacities are at a higher level of modality and are not reducible to causal laws, as causal laws are not reducible to laws of association that describe mere regularities; but unlike laws of association, capacities are real.

This chapter gives a concrete example of a question of current scientific interest where capacities matter: ‘Do the Bell inequalities describing

Einstein–Podolsky–Rosen (EPR) experiments show that causality is incompatible with quantum mechanics?’ The question cannot be answered if we rely on probabilistic theories of causality and laws of associations alone. It takes the concept of capacity and related notions of how capacities operate even to formulate the problem correctly. Econometrics models with correlated errors, it is shown, can explain the EPR results when the correlations are given a capacity interpretation.

This chapter argues that one cannot get knowledge of causes from equations and associations alone, using critical analyses of theoretical examples in physics and of attempts in the philosophy of science and economics (e.g. Granger causality and various probabilistic theories of causality) to reduce causal claims to probabilities. Old causal knowledge must be supplied for new causal knowledge to be had. Analysis of experimental methods and actual experiments (Stanford Gravity Probe) show how this can be done.

Argues for the irreducibility of causal laws to laws of association, probabilistic or deterministic. Statistical or probabilistic analyses of causality, which typically require that the cause increase or alter the probability of the effect, cannot succeed because causes increase the probability of their effects only in situations that exhibit causal homogeneity with respect to that effect (Simpson's paradox). This condition must enter the definition of an effective strategy, which is why causal laws are ineliminable for scientifically grounded interventions in nature.