Beyond Coefficients: Forecast-Necessity Testing for Interpretable Causal Discovery in Nonlinear Time-Series Models

This paper proposes a novel method called forecast-necessity testing for interpretable causal discovery in nonlinear time-series models. It aims to move beyond traditional coefficients to better understand complex causal relationships.