Causal cycles, causal intervals, and measurement in over-time studies

If over-time data are used to model XàY causal relationships, the measurement (or “recording”) interval should match (or at least approximate) the actual causal (or “existence”) interval for X’s effect on Y. I discuss this issue in the context of causal cycles of events and give three examples involving hurricanes, job change and adoption and implementation of new technology. I conclude with some considerations and recommendations for matching measurement to causal intervals in over-time research.