Forecast-Necessary Causal Discovery for Nonlinear Political Panel Data: Feedback, Functional Form, and the Dynamics of Democratization
stat.ME, cs.LG
Submitted: 2026-09-28
Updated: 2026-09-28
Project page: https://christophm.github.io/interpretable-ml-book
Terminology
Sources
- Beyond Coefficients: Forecast-Necessity Testing for Interpretable Causal Discovery in Nonlinear Time-Series Models
- Function-Valued Causal Influence in Nonlinear Time Series
- From Causal Discovery to Dynamic Causal Inference in Neural Time Series
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