5 papers
Forecast-Necessary Causal Discovery for Nonlinear Political Panel Data: Feedback, Functional Form, and the Dynamics of Democratization
Michael Coppedge, Dmitry Zaytsev, Valentina Kuskova
A non-significant coefficient in a dynamic panel model need not imply the absence of a relationship. It may instead reflect heterogeneous effects averaged toward zero, reciprocal d…
Trajectory-Aware Node Contributions and the Limits of Static Controllability
Valentina Kuskova, Dmitry Zaytsev, Michael Coppedge
A recurring data mining task in complex networks is to determine how individual nodes contribute to system behavior. Existing approaches rely on either static-graph centralities or…
Function-Valued Causal Influence in Nonlinear Time Series
Valentina V. Kuskova, Dmitry Zaytsev, Michael Coppedge
Causal discovery in time series is increasingly performed using nonlinear machine-learning models, yet the resulting causal relationships are almost always summarized by scalar edg…
Trajectory-Aware Reliability Modeling of Democratic Systems
Dmitry Zaytsev, Valentina Kuskova, Michael Coppedge
Failures in complex systems often emerge through gradual degradation and the propagation of stress across interacting components rather than through isolated shocks. Democratic sys…
From Causal Discovery to Dynamic Causal Inference in Neural Time Series
Dmitry Zaytsev, Valentina Kuskova, Michael Coppedge
Time-varying causal models provide a powerful framework for studying dynamic scientific systems, yet most existing approaches assume that the underlying causal network is known a p…