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cs.LG2024
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning
Shuyu Dong, Michèle Sebag, Kento Uemura +4
Causal learning tackles the computationally demanding task of estimating causal graphs. This paper introduces a new divide-and-conquer approach for causal graph learning, called DC…
stat.ML2024
Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges
Audrey Poinsot, Alessandro Leite, Nicolas Chesneau +2
This paper provides a comprehensive review of deep structural causal models (DSCMs), particularly focusing on their ability to answer counterfactual queries using observational dat…