CauSim turns scarce causal reasoning labels into scalable supervised data by having LLMs incrementally construct complex executable structural causal models.
Learning bayesian networks with the bnlearn r package.Journal of Statistical Software, 35(3):1–22
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BFS-based LLM framework reduces causal graph discovery queries from quadratic to linear while incorporating observational data and reporting state-of-the-art results on real graphs.
Econometric methods impose clear temporal rules on causal structures from time series, whereas causal ML algorithms produce denser graphs that recover more identifiable causal effects in UK COVID-19 policy data.
citing papers explorer
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CauSim: Scaling Causal Reasoning with Increasingly Complex Causal Simulators
CauSim turns scarce causal reasoning labels into scalable supervised data by having LLMs incrementally construct complex executable structural causal models.
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Efficient Causal Graph Discovery Using Large Language Models
BFS-based LLM framework reduces causal graph discovery queries from quadratic to linear while incorporating observational data and reporting state-of-the-art results on real graphs.
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Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policies
Econometric methods impose clear temporal rules on causal structures from time series, whereas causal ML algorithms produce denser graphs that recover more identifiable causal effects in UK COVID-19 policy data.