Abstract:
In this paper, we introduce the nonparametric, direct potential outcome system as a foundational framework for analyzing dynamic causal effects of assignments on outcomes in observational time series settings. Using this framework, we provide conditions under which common predictive time series estimands, such as the impulse response function, generalized impulse response function, local projection, and local projection instrument variables, have a nonparametric causal interpretation in terms of such dynamic causal effects.