arXiv:2605.26408v1 Announce Type: new Abstract: 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 bottlenec
The increasing adoption and complexity of nonlinear machine-learning models in time series analysis necessitates more sophisticated methods for interpreting causal relationships beyond simple scalar scores.
This work introduces a more rigorous and nuanced method for understanding causality in AI models, moving beyond simplistic interpretations and enabling deeper insights into complex system behaviors.
The previous practice of summarizing complex causal relationships with scalar scores is challenged, paving the way for function-valued causal influence that captures state-dependent effects in AI models.
- · AI researchers
- · Data scientists
- · Complex systems modeling
- · Finance and economics
- · Simplistic causal inference methods
- · Models reliant on single-score causality
- · AI interpretability tools without function-valued capabilities
Improved interpretability and trustworthiness of nonlinear AI models for time series data.
Better decision-making in critical applications like predictive maintenance, climate modeling, and financial forecasting due to more accurate causal insights.
The development of new AI architectures and algorithms specifically designed to natively incorporate and output function-valued causal influences.
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