Abstract: We define a foundational nonparametric ``potential system'' to quantify how, in the presence of confounders, assignments at time $t$ causally impact outcomes at time $t+h$, where $h\ge 0$ is a time horizon. The analysis is based on a series of assumptions and definitions involving potential outcomes, non-anticipation and sequential randomization.  It directly connects familiar time series objects like impulse response functions to average treatment effects and, more generally, the time series causality literature to the nonparametric causal inference literature based either on population or design-based inference strategies.