Cause-Effect Pairs in Time Series with a Focus on Econometrics

This chapter addresses the problem of identifying the causal structure between two time-series processes. We focus on the setting typically encountered in econometrics, namely stationary or difference-stationary multiple autoregressive processes with additive white noise terms. We review different methods and algorithms, distinguishing between methods that filter the series through a vector autoregressive (VAR) model and methods that apply causal search directly to time series data. We also propose an additive noise model search algorithm tailored to the specific task of distinguishing among causal structures on time series pairs, under different assumptions, among which causal sufficiency.