Function-Valued Causal Influence in Nonlinear Time Series
Causal discovery in time series is increasingly performed using nonlinear machine-learning models, yet the resulting causal relationships are almost always summarized by scalar edge scores. We argue that this practice obscures the true object learned by nonlinear autoregressive models: a state-dependent function whose effect varies across regimes, magnitudes, and contexts. We formalize function-valued causal influence for additive, contribution-decomposable architectures and show that scalar causal scores constitute a severe information bottleneck, conflating between-state variation with within-state residual noise. Using Neural Additive Vector Autoregression as a representative architect...
| Authors | Valentina V. Kuskova, Dmitry Zaytsev, Michael Coppedge |
|---|---|
| Primary category | cs.LG |
| Published | 26 May 2026 |
| Last updated at source | 5 October 2026 |
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