Zaytsev, D., Kuskova, V., & Coppedge, M. (2026). Stability-Constrained Regime-Aware Forecasting for Heterogeneous Panel Time Series. NeurIPS 2026.
Kuskova, V., Zaytsev, D., Coppedge, M. (2026). Function-Valued Causal Influence in Nonlinear Time Series. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306: 61873–61898 and arXiv preprint arXiv:2605.26408.
Zaytsev, D., Kuskova, V., & Coppedge, M. (2026). From Causal Discovery to Dynamic Causal Inference in Neural Time Series. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026) and arXiv preprint arXiv:2603.20980.
Kuskova, V., Zaytsev, D., & Coppedge, M. (2026). Beyond Coefficients: Forecast-Necessity Testing for Interpretable Causal Discovery in Nonlinear Time-Series Models. The International FLAIRS Conference Proceedings, 39(1) and arXiv preprint arXiv: 2604.18751.
Zaytsev, D., Kuskova, V., & Coppedge, M. (2026). Trajectory-Aware Reliability Modeling of Democratic Systems. The 31st ISSAT International Conference on Reliability & Quality in Design Proceedings and arXiv preprint arXiv:2604.20127.
Coppedge, M., Zaytsev, D., & Kuskova, V. (2026). Forecast-Necessary Causal Discovery for Nonlinear Political Panel Data: Feedback, Functional Form, and the Dynamics of Democratization. arXiv preprint arXiv:2609.34115.
Kuskova, V., Zaytsev, D., & Coppedge, M. (2026). When Are Neural Interaction Discoveries Real? Identifiability, Recoverability, and a Pre-Fit Diagnostic. arXiv preprint arXiv:2606.08390.
Kuskova, V., Zaytsev, D., & Coppedge, M. (2026). Trajectory-Aware Node Contributions and the Limits of Static Controllability. arXiv preprint arXiv:2606.03067.
