10 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…
Signed Exposure: Fair Routing of Algorithmic Attention When Attention Can Harm
Daria Leshchikova, Valentina V. Kuskova, Dmitry Zaytsev +1
Fairness-of-exposure treats algorithmic attention as a good to be distributed equitably. But when an autonomous agent initiates contact, attention is signed: it delivers value to a…
Two-sided receptivity to conversational AI agents in online dating: Bilingual survey data from Fledge.Love
Daria Leshchikova, Valentina V. Kuskova, Dmitry Zaytsev +1
Autonomous conversational agents and generative-AI features are being added to online dating platforms faster than public evidence about user attitudes can accumulate, and the scar…
Delegation Asymmetry in Agentic Recommender Systems: Measuring Two-Sided Receptivity in Online Dating
Daria Leshchikova, Valentina V. Kuskova, Dmitry Zaytsev +1
Autonomous LLM agents that converse on a user's behalf are an emerging design pattern in matching platforms, yet their viability depends on a condition rarely examined: users must…
When Are Neural Interaction Discoveries Real? Identifiability, Recoverability, and a Pre-Fit Diagnostic
Valentina Kuskova, Dmitry Zaytsev, Michael Coppedge
When a neural time-series model reports that one variable modulates another's effect on a target, is the discovered interaction a property of the data or an artifact of model flexi…
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…
Learning AI Without a STEM Background: Mixed-Methods Evidence from a Diverse, Mixed-Cohort AIED Program
Valentina Kuskova, Dmitry Zaytsev, Richard Johnson
Despite growing interest in AI education, most AIED initiatives remain narrowly targeted toward STEM-prepared students, limiting participation by non-STEM learners and adults seeki…
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…