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.

本文讨论时间在社会科学因果模型中的作用。目的是强调时间敏感的因果模型与无时间模型相比的重要性。时间和因果关系之间的关系很重要，尽管它是复杂的，正如科学哲学中的辩论所表明的那样。特别是，一个悬而未决的问题是是否可以从时间排序中推导出因果排序。这篇文章研究了如何在人口学和经济学中考虑时间作为社会科学的例子，在这些社会科学中，对时间的考虑可能会有所不同。然后，我们将结构因果建模作为一种建模策略，虽然本质上不是基于时间信息，但可以或多或少明确地将其合并。特别是，我们认为，时间信息是有用的，因为它被置于正确的因果结构中，从而进一步证实了解释所考虑现象的因果机制或生成过程。尽管出于解释目的，变量的因果顺序比时间顺序更相关，但在建立因果顺序时，研究人员仍应考虑原因和结果的时间模式，因为这些通常是插曲而不是单个事件。特别是出于这个原因，是时候把时间放在我们因果模型的核心了。尽管出于解释目的，变量的因果顺序比时间顺序更相关，但在建立因果顺序时，研究人员仍应考虑原因和结果的时间模式，因为这些通常是插曲而不是单个事件。特别是出于这个原因，是时候把时间放在我们因果模型的核心了。尽管出于解释的目的变量的因果顺序比时间顺序更相关，但在建立因果顺序时，研究人员仍应考虑原因和结果的时间模式，因为这些通常是插曲而不是单个事件。特别是出于这个原因，是时候把时间放在我们因果模型的核心了。