Function-Valued Causal Influence in Nonlinear Time Series
Standard methods reduce complex nonlinear causal structures to single scalar scores, losing vital information like thresholds and saturation. This work recovers state-dependent causal influence by estimating response functions directly from trained models using an Individual Conditional Expectation (ICE) framework. The finding is that scalar summaries obscure essential regime-specific behaviors.