Time and causality in the social sciences

Abstract

This article deals with the role of time in causal models in the social sciences. The aim is to underline the importance of time-sensitive causal models, in contrast to time-free models. The relation between time and causality is important, though a complex one, as the debates in the philosophy of science show. In particular, an outstanding issue is whether one can derive causal ordering from time ordering. The article examines how time is taken into account in demography and in economics as examples of social sciences in which considerations about time may diverge. We then present structural causal modeling as a modeling strategy that, while not essentially based on temporal information, can incorporate it in a more or less explicit way. In particular, we argue that temporal information is useful to the extent that it is placed in a correct causal structure, thus further corroborating the causal mechanism or generative process explaining the phenomenon under consideration. Despite the fact that the causal ordering of variables is more relevant than the temporal order for explanatory purposes, in establishing causal ordering the researcher should nevertheless take into account the time-patterns of causes and effects, as these are often episodes rather than single events. For this reason in particular, it is time to put time at the core of our causal models.