AINeutralarXiv – CS AI · 3d ago7/10
🧠Researchers prove that large language models fundamentally cannot perform causal discovery through standard training methods, establishing this limitation as intrinsic to supervised learning rather than a model-specific flaw. They propose Agentic Causal Bayesian Optimization (A-CBO), which bypasses this constraint by using frozen language models as query oracles within an external optimization loop, achieving superior performance on causal inference benchmarks.
AINeutralarXiv – CS AI · 2d ago6/10
🧠A comprehensive study of Markov boundaries in tabular prediction reveals that while oracle boundaries significantly improve model performance, practical causal discovery methods fail to recover them cost-effectively. The research identifies fundamental misalignments between structural recovery optimization and predictive performance, suggesting that prediction-focused feature selection requires different approaches than theoretical assumptions propose.
AINeutralarXiv – CS AI · 2d ago6/10
🧠Researchers propose Test-Time Training for Supervised Causal Learning (TTT-SCL), a framework addressing critical limitations in causal discovery by generating test-specific training sets. The approach significantly improves performance gaps between synthetic benchmarks and real-world applications while enhancing robustness to distribution shifts.
AINeutralarXiv – CS AI · 2d ago6/10
🧠Researchers introduce CausaLab, a benchmarking environment that tests whether LLM agents can both solve causal discovery problems and accurately recover the underlying causal mechanisms. Experiments reveal a significant gap between prediction accuracy (92%) and structural causal model recovery (0.471 F1 score), exposing limitations in current AI systems' ability to perform rigorous scientific reasoning.
🧠 GPT-5
AINeutralarXiv – CS AI · 3d ago6/10
🧠Researchers present a novel causal discovery framework that combines multiple structure learning algorithms with heterogeneous effect estimation to identify drivers of undesirable health outcomes across patient subpopulations. Validated through healthcare applications examining emergency department revisits and hospital readmissions, the framework reveals that intervention effectiveness varies significantly by patient characteristics, prioritizing chronic disease management and care coordination as key targets.
AINeutralarXiv – CS AI · 4d ago6/10
🧠Researchers introduce MAC, a multi-agent framework that combines statistical causal discovery with large language models to identify relationships between variables more accurately than existing methods. By using autonomous agent debate and adversarial reasoning, MAC outperforms both traditional statistical and single-agent LLM approaches across multiple benchmark datasets.
🧠 Gemini
AINeutralarXiv – CS AI · May 126/10
🧠Researchers present Hierarchical Causal Abduction (HCA), a framework that makes Model Predictive Control decisions interpretable by combining physics-informed reasoning, optimization evidence, and causal discovery. The method achieves 53% higher explanation accuracy than existing approaches across industrial control applications, addressing a critical barrier to deploying AI in safety-critical infrastructure.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce TTCD (Transformer Integrated Temporal Causal Discovery), a novel machine learning framework designed to identify causal relationships in non-stationary time series data. The method combines transformer-based feature learning with causal structure inference, demonstrating superior performance over existing approaches on synthetic and real-world datasets.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce SVAR-FM, a framework that uses physics-based simulators to discover causal relationships in time series data by treating simulation interventions as Pearl's do operator. The method recovers correct causal directions where observational methods fail due to confounding, with theoretical guarantees and empirical validation across multiple scientific domains.
AINeutralarXiv – CS AI · May 115/10
🧠Researchers present a solution for selecting cost-effective experiments to narrow uncertainty bounds on partially identifiable causal effects from observational data. They formalize this as an NP-hard optimization problem and develop pruning algorithms that eliminate 50-88% of candidate experiments without exhaustive computation, demonstrated on real epidemiological datasets.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers introduce Mask2Cause, a deep learning framework that discovers causal relationships in time series data by integrating causal graph extraction directly into the forecasting process. The method achieves state-of-the-art results while reducing model parameters by over 70% compared to existing approaches.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers introduce CFM-SD, a causal discovery method that leverages physical simulators to identify cause-and-effect relationships in scientific domains while handling latent confounders—a common problem in molecular design and materials science. The approach achieves significantly higher accuracy than existing methods and demonstrates practical improvements in real-world applications like toxicity prediction and battery optimization.
AINeutralarXiv – CS AI · Mar 27/1013
🧠Researchers introduce E-CIT (Ensemble Conditional Independence Test), a new framework that significantly reduces computational costs in causal discovery by partitioning data into subsets and aggregating results. The method achieves linear computational complexity while maintaining competitive performance, particularly on real-world datasets.
AINeutralarXiv – CS AI · Mar 54/10
🧠Researchers propose a new pipeline to extract causal relationships from large language models by sampling documents, identifying events, and using causal discovery methods. The approach aims to reveal the causal hypotheses that LLMs assume rather than establishing real-world causality.
AINeutralarXiv – CS AI · Feb 274/105
🧠Researchers introduce Causal Computational Asymmetry (CCA), a new method for identifying causal relationships by training neural networks in both directions and determining causality based on which direction converges faster during optimization. The method achieved 26/30 correct causal identifications across synthetic benchmarks and is embedded in a broader Causal Compression Learning framework.
AINeutralarXiv – CS AI · Mar 24/106
🧠Researchers developed a framework for causal discovery in longitudinal data systems that addresses real-world workflow constraints by incorporating institutional protocols and timeline structures. The method was tested on a large Japanese health screening dataset with over 100,000 individuals, showing improved structural interpretability without requiring domain-specific specifications.
AINeutralarXiv – CS AI · Mar 24/109
🧠Researchers introduce FLOP, a new causal discovery algorithm for linear models that significantly reduces computation time through fast parent selection and Cholesky-based score updates. The algorithm achieves near-perfect accuracy in standard benchmarks and makes discrete search approaches viable for causal structure learning.
$NEAR
