A unified framework for the social sciences

This paper offers four main arguments about the nature of causation in the social sciences. First, contrary to most recent work, I argue that there is a unitary conception of causation: a cause raises the probability of an event. This understanding of causation, borrowed from but not wedded to Bayesian inference, provides common semantic ground on which to base a reconstruction of causation. I argue, second, that rather than thinking about causation as a series of discrete types or distinct rules we ought to re-conceptualize this complex form of argument as a set of logical criteria applying to all arguments that are causal in nature (following the foregoing definition), across fields and across methods. Here, it is helpful to distinguish between the formal properties of a causal argument and the methods by which such an argument might be tested, the research design. Sixteen criteria apply to the former and seven criteria apply to the latter, as I show in the body of the paper. In summary, causation in the social sciences is both more diverse and more unified than has generally been recognized.