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When I was busily expounding the statistical-relevance model … I was aware that explanation involves causality, but I hoped that the required causal relations could be fully explicated by means of … statistical concepts. A decade later, I was quite thoroughly convinced that this hope could not be fulfilled. Along with this realization came the recognition that statistical relevance relations, in and of themselves, have no explanatory force. They have significance for scientific explanation only insofar as they provide evidence for causal relations … causal explanation, I argued, must appeal to such mechanisms as causal propagation and causal interactions, which are not explicated in statistical terms.262

In Salmon’s view, this failure to rescue the D-N model by rendering it in statistical or probabilistic terms led to the emergence of an alternative approach to scientific explanation. Whereas the D-N model explained events through general laws of nature, the alternative approach to explanation “made a strong identification between causality and explanation. Roughly and briefly, to explain an event is to identify its cause. The examples that furnish the strongest intuitive basis for this conception are cases of explanations of particular occurrences—for instance, the sinking of the Titanic.”263

Salmon adds that the mechanism-based approach that he came to favor “makes explanatory knowledge into knowledge of the hidden mechanisms by which nature works. It goes beyond phenomenal descriptive knowledge into knowledge of things that are not open to immediate inspection. Explanatory knowledge opens up the black boxes of nature to reveal their inner workings. It exhibits the ways in which the things we want to explain come about.”264

DEFINING CAUSAL MECHANISMS

The approach of explaining phenomena via causal mechanisms has gained a wide following among social scientists and philosophers of science. 265 The growing interest in causal mechanisms is of particular significance because it is shared by scholars who disagree on other important questions regarding theory and methodology. Yet there is no agreement on an exact definition of “causal mechanism.” Some have defined causal mechanisms as being essentially indistinguishable from theories; Peter Hedstrom and Richard Swedburg, for example, define causal mechanisms as “analytical constructs that provide hypothetical links between observable events.”266 We prefer a scientific realist definition that places causal mechanisms on the ontological level.267 In this view, theories and explanations are hypothesized models of how underlying mechanisms work. Roy Bhaskar, for example, states that “the construction of an explanation for … some identified phenomenon will involve the building of a model … which if it were to exist and act in the postulated way would account for the phenomenon in question.” 268 Similarly, James Mahoney has defined a causal mechanism as “an unobservable entity that—when activated—generates an outcome of interest.” 269 This introduces the notion that causal mechanisms are sufficient, in specific contexts, to bring about outcomes. Wesley Salmon also puts causal mechanisms on the ontological level, stating that “an intersection of two processes is a causal interaction if both processes are modified in the intersection in ways that persist beyond the point of intersection … causal processes are capable of transmitting energy, information, and causal influence from one part of spacetime to another.”270

Building on these definitions, we define causal mechanisms as ultimately unobservable physical, social, or psychological processes through which agents with causal capacities operate, but only in specific contexts or conditions, to transfer energy, information, or matter to other entities. In so doing, the causal agent changes the affected entity’s characteristics, capacities, or propensities in ways that persist until subsequent causal mechanisms act upon it. If we are able to measure changes in the entity being