Causality Regularity Models of Causation

Causality refers to the relationship between events where one set of events (the effects) is a direct consequence of another set of events (the causes). Causal inference is the process by which one can use data to make claims about causal relationships. Since inferring causal relationships is one of the central tasks of science, it is a topic that has been heavily debated in philosophy, statistics, and the scientific disciplines. In this article, we review the models of causation and tools for causal inference most prominent in the social sciences, including regularity approaches, associated with David Hume, and counterfactual models, associated with among many others. One of the most notable developments in the study of causation is the increasing unification of disparate methods around a common conceptual and mathematical language that treats causality in counterfactual terms-i.e., the Neyman-Rubin model. We discuss how counterfactual models highlight the deep challenges involved in making the move from correlation to causation, particularly in the social sciences where controlled experiments are relatively rare. Until the advent of counterfactual models, causation was primarily defined in terms of observable phenomena. It was philosopher David Hume in the eighteenth century who began the modern tradition of regularity models of causation by defining causation in terms of repeated " conjunctions " of events. In An Enquiry into Human Understanding (1751), Hume argued that the labeling of two particular events as being causally related rested on an untestable metaphysical assumption. Consequently, Hume argued that causality could only be adequately defined in terms of empirical regularities involving classes of events. How could we know that a flame caused heat, Hume asked? Only by calling " to mind their constant conjunction in all past instances. Without further ceremony, we call the one cause and the other effect, and infer the existence of one from that of the other. " Hume argued that three empirical phenomenon were necessary for inferring causality: contiguity (" the cause and effect must be contiguous in time and space "), succession (" the cause must be prior to the effect "), and constant conjunction (" there must be a constant union betwixt the cause and effect "). Under this framework, causation was defined purely in terms of empirical criteria, rather than unobservable assumptions. In other words, Hume's definition of causation and his mode of inference were one and the same. John Stewart Mill, who shared the regularity view …