A framework generates synthetic neuroimages with explicit causal control via volumetric ROI changes to produce ground-truth data for benchmarking causal AI in neuroimaging.
gcastle: A python toolbox for causal discovery
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FML-Bench shows a simple greedy hill-climber nearly matches tree search on dense-opportunity tasks while an adaptive agent that broadens search on stagnation outperforms six baselines across 18 tasks.
CauTion uses consensus filtering on algorithm ensembles, trust-calibrated LLM arbitration only on uncertain edges, and cycle repair to outperform baselines on six datasets while remaining robust to LLM mistakes.
The paper demonstrates that assuming the quantile partial effect lies in a finite linear span enables causal identifiability from observational data, with applications to bivariate and multivariate causal discovery using basis tests and Fisher information.
citing papers explorer
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A Neuroimaging Simulation Framework for Developing and Evaluating Causal AI
A framework generates synthetic neuroimages with explicit causal control via volumetric ROI changes to produce ground-truth data for benchmarking causal AI in neuroimaging.
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FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics
FML-Bench shows a simple greedy hill-climber nearly matches tree search on dense-opportunity tasks while an adaptive agent that broadens search on stagnation outperforms six baselines across 18 tasks.
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CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery
CauTion uses consensus filtering on algorithm ensembles, trust-calibrated LLM arbitration only on uncertain edges, and cycle repair to outperform baselines on six datasets while remaining robust to LLM mistakes.
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Causal Discovery via Quantile Partial Effect
The paper demonstrates that assuming the quantile partial effect lies in a finite linear span enables causal identifiability from observational data, with applications to bivariate and multivariate causal discovery using basis tests and Fisher information.