causal discovery

I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variables AAAI 2026 Information-theoretic signatures of causality in Bayesian networks and hypergraphs CLeaR 2026 Interpretable Causal Discovery via Causal-Effect Constraints UAI 2026 iTAG: Inverse Design for Natural Text Generation with Accurate Causal Graph Annotations ACL 2026 Learning a Spatial Partitioning and its Causal Relations from Temporal Data CLeaR 2026 Learning Stable Digraphs from Sparse-Input Linear Structural Causal Models UAI 2026 Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach UAI 2026 Local Markov Equivalence for PC-style Local Causal Discovery and Identification of Controlled Direct Effects CLeaR 2026 MetaCaDI: A Meta-Learning Framework for Causal Discovery from Multiple Environments with Unknown Interventions UAI 2026 Meta-Dependence in Conditional Independence Testing UAI 2026 OrcheCause Agent: From Textual Knowledge to End-to-End Causal Inference AAAI 2026 POLICYGRID: Causal Discovery for Adaptive Policy Optimization in Embodied Agents (Student Abstract) AAAI 2026 Probabilistic Edge Modulation for High-Dimensional Causal Discovery UAI 2026 REACT-LLM: A Benchmark for Evaluating LLM Integration with Causal Features in Clinical Prognostic Tasks AAAI 2026 Recursive Causal Discovery (Abstract Reprint) AAAI 2026 Relaxing Faithfulness with Intervention-Only Causal Discovery UAI 2026 Retrieving Classes of Causal Orders with Inconsistent Knowledge Bases CLeaR 2026 Robust Causal Discovery Under Imperfect Structural Constraints AAAI 2026 Robust Weighted Triangulation of Causal Effects Under Model Uncertainty UAI 2026 Sparse Additive Model Pruning for Order-Based Causal Structure Learning AAAI 2026 Structure Learning for Unfaithful Distributions: The Minimal Dependence Faithfulness UAI 2026 The Price of Valid Inference After Causal Discovery UAI 2026 The relative value of interventional and observational samples in Bayesian Causal Linear Gaussian Models UAI 2026 TimeCAP: A Channel-Aware Pre-Training Framework for Multivariate Time Series Forecasting AAAI 2026 Uncovering Bias Paths with LLM-guided Causal Discovery: An Active Learning and Dynamic Scoring Approach AAAI 2026