Two new research papers explore advanced causal inference techniques for complex spatiotemporal data. The first paper introduces a spatiotemporal proximal causal inference framework to address hidden confounding and interference, utilizing transformer-based encoders for proxy learning. The second paper proposes Large Causal Models (LCMs) as a foundation-model paradigm for temporal causal discovery, demonstrating that these pre-trained neural architectures can scale effectively to higher variable counts and maintain strong performance, particularly in out-of-distribution settings. AI
IMPACT These papers advance foundational research in causal inference, potentially improving AI's ability to understand and predict complex systems.
RANK_REASON Two academic papers published on arXiv detailing novel methods for causal inference.
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- IArxiv
- Large Causal Models
- Nikolaos Kougioulis
- ScienceCast
- Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interference
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