Temporal information plays a major role in human causal inference. We present a rational framework for causal induction from events that take place in continuous time. We define a set of desiderata for such a framework and outline a strategy for satisfying these desiderata using continuous-time stochastic processes. We develop two specific models within this framework, illustrating how it can be used to capture both generative and preventative causal relationships as well as delays between cause and effect. We evaluate one model through a new behavioral experiment, and the other through a comparison to existing data.