Authors: Menghua Wu, Yujia Bao, Regina Barzilay, Tommi Jaakkola

Published on: February 02, 2024

Impact Score: 8.27

Arxiv code: Arxiv:2402.01929

Summary

• What is new: A new causal discovery framework utilizing deep learning for faster, more generalizable causal structure inference across diverse datasets.
• Why this is important: Existing causal discovery algorithms are slow, require large amounts of data, and are fragile across different datasets.
• What the research proposes: A pretrained deep learning model that improves upon classical algorithms by quickly analyzing smaller variable subsets for causal discovery.
• Results: Achieved state-of-the-art performance on both synthetic and realistic datasets, generalized to unseen data generating mechanisms, and significantly increased inference speed.

Technical Details

Technological frameworks used: Deep learning-based causal discovery

Models used: Pretrained models on smaller variable subsets

Data used: Synthetic and realistic datasets

Potential Impact

Scientific research institutions, policy-making bodies, data-driven industries.

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