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Mapping and Advancing the Scalability-Accuracy Frontier of Nonlinear Causal Discovery
Scalable nonlinear causal discovery requires methods that combine flexible mechanism estimators with efficient search over large graph spaces. Several algorithmic families have been proposed to address this challenge, yet their accuracy-runtime trade-offs remain poorly understood. We empirically com
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