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cs.LG2024
Generative Intervention Models for Causal Perturbation Modeling
Nora Schneider, Lars Lorch, Niki Kilbertus +2
We consider the problem of predicting perturbation effects via causal models. In many applications, it is a priori unknown which mechanisms of a system are modified by an external…
cs.LG2024
Standardizing Structural Causal Models
Weronika Ormaniec, Scott Sussex, Lars Lorch +2
Synthetic datasets generated by structural causal models (SCMs) are commonly used for benchmarking causal structure learning algorithms. However, the variances and pairwise correla…