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

📰 ArXiv cs.AI

Learn to move beyond coefficients for interpretable causal discovery in nonlinear time-series models using forecast-necessity testing

advanced Published 22 Apr 2026

Action Steps

Apply forecast-necessity testing to nonlinear time-series models to evaluate causal relevance
Use regularized neural autoregressive models to discover causal relationships in time-series data
Evaluate the statistical significance of causal scores produced by these models
Compare the results of forecast-necessity testing with traditional coefficient-based methods
Configure and test the models using techniques such as cross-validation to ensure robust results

Who Needs to Know This

Data scientists and researchers working with nonlinear time-series models can benefit from this approach to improve the interpretation of their models' outputs

Key Insight

💡 Forecast-necessity testing provides a more accurate and reliable method for evaluating causal relevance in nonlinear time-series models than traditional coefficient-based methods

Key Takeaways

Learn to move beyond coefficients for interpretable causal discovery in nonlinear time-series models using forecast-necessity testing

Full Article

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

Abstract:
arXiv:2604.18751v1 Announce Type: cross Abstract: Nonlinear machine-learning models are increasingly used to discover causal relationships in time-series data, yet the interpretation of their outputs remains poorly understood. In particular, causal scores produced by regularized neural autoregressive models are often treated as analogues of regression coefficients, leading to misleading claims of statistical significance. In this paper, we argue that causal relevance in nonlinear time-series mod

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