A nonparametric sensitivity framework supplies bounds on the controlled direct effect of contagion in fixed-in-time networks by quantifying the latent homophily strength required to explain away observed connected-dyad associations, with a simulation study and application to 2008 U.S. House TARP vot
Causation, Prediction , and Search
3 Pith papers cite this work. Polarity classification is still indexing.
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CausalSteward is a multi-agent divide-conquer-combine framework for causal discovery that integrates prior knowledge with data-driven methods in a human-in-the-loop setup for high-dimensional data.
A framework using structural causal models simulates parametric drifts to evaluate classifier robustness more realistically than static tests or noise perturbations.
citing papers explorer
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A Sensitivity Framework for Identifying Contagion under Latent Homophily for Fixed-in-Time Network Analyses, with an Application to U.S. House Congressional Voting
A nonparametric sensitivity framework supplies bounds on the controlled direct effect of contagion in fixed-in-time networks by quantifying the latent homophily strength required to explain away observed connected-dyad associations, with a simulation study and application to 2008 U.S. House TARP vot
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CausalSteward: An Agentic Divide-Conquer-Combine Copilot for Causal Discovery
CausalSteward is a multi-agent divide-conquer-combine framework for causal discovery that integrates prior knowledge with data-driven methods in a human-in-the-loop setup for high-dimensional data.
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Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation
A framework using structural causal models simulates parametric drifts to evaluate classifier robustness more realistically than static tests or noise perturbations.