Buch, Englisch, Format (B × H): 155 mm x 235 mm
4th Workshop, CADIS 2026, Tonantzintla, Mexico, November 30 – December 1, 2026, Proceedings
Buch, Englisch, Format (B × H): 155 mm x 235 mm
Reihe: Communications in Computer and Information Science
ISBN: 978-3-032-44865-1
Verlag: Springer
This book constitutes the refereed proceedings of the 4th Workshop on Causal Discovery, CADIS 2026, held in Tonantzintla, Mexico, during November 30 – December 1, 2026.
The 11 full papers, 1 short paper, 2 invited keynotes and 1 paper abstract included in this volume were carefully reviewed and selected from 14 submissions. The papers cover the following topical sections: Fundamentals and Algorithms for Causal Discovery; and Applications.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
.- Fundamentals and Algorithms for Causal Discovery.
.- Can Resampled Ensembles Improve Causal Discovery Performance?.
.- Non-Identifiability of the Interventional Transition Kernel in Offline Reinforcement Learning.
.- Causal Discovery with Diffusion-Based Data Augmentation under Causal Constraints.
.- Structural limits of observational learning for interventional competence.
.- When Imputation Changes the Graph: Causal Discovery in Sparse BDI-II Symptom Trajectories.
.- Structural Agreement Between MMPC and GES Using Sociodemographic and Anthropometric Data from Schoolchildren.
.- Automatic Causal Fairness Analysis with LLM-Generated Reporting.
.- Quantifying Canonical-DAG Approximation Bias in Causal Systems with Potential Confounding.
.- Applications.
.- Causal Discovery in Galaxies via a Weighted Multi-Algorithmic Consensus.
.- Dynamic State Masking for Proximal Policy Optimization Algorithm.
.- Effect of Synthetic-Data Replication and Coverage on Counterfactual Decision-Making.
.- Impact of prior knowledge in network structure learning for diabetes related factors.
.- Diagnosing Causal Reasoning Failures in Large Language Models: Implications for LLM-Assisted Causal Discovery.
