Causal-Audit: A Framework for Risk Assessment of Assumption Violations in Time-Series Causal Discovery
Institute for Systems and Robotics, Instituto Superior Técnico（系统与机器人研究所，里斯本高等理工学院）；Digital Scholarship at Oxford, University of Oxford（牛津大学数字学术中心）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）
Marco Ruiz, Miguel Arana-Catania, David R. Ardila, Rodrigo Ventura
备注：
28 pages, 10 figures, 15 tables. Being submitted to Journal of Causal Inference JCI
Time-series causal discovery methods rely on assumptions such as stationarity, regular sampling, and bounded temporal dependence. When these assumptions are violated, structure learning can produce confident but misleading causal graphs without warning. We introduce Causal-Audit, a framework that formalizes assumption validation as calibrated risk assessment. The framework computes effect-size diagnostics across five assumption families (stationarity, irregularity, persistence, nonlinearity, and confounding proxies), aggregates them into four calibrated risk scores with uncertainty intervals, and applies an abstention-aware decision policy that recommends methods (e.g., PCMCI+, VAR-based Granger causality) only when evidence supports reliable inference. The semi-automatic diagnostic stage can also be used independently for structured assumption auditing in individual studies. Evaluation on a synthetic atlas of 500 data-generating processes (DGPs) spanning 10 violation families demonstrates well-calibrated risk scores (AUROC > 0.95), a 62% false positive reduction among recommended datasets, and 78% abstention on severe-violation cases. On 21 external evaluations from TimeGraph (18 categories) and CausalTime (3 domains), recommend-or-abstain decisions are consistent with benchmark specifications in all cases. An open-source implementation of our framework is available.
AutoCause: A Python framework that automates expert decisions in environmental time-series causal discovery
ISR-Lisbon, Instituto Superior Técnico（里斯本信号与系统研究所，里斯本高等理工学院）；Digital Scholarship at Oxford, University of Oxford（牛津大学牛津数字学术中心）；Jet Propulsion Lab., Caltech（加州理工学院喷气推进实验室）
Assumption violations in causal discovery and the robustness of score matching
Università di Genova（热那亚大学）；AWS（亚马逊云科技）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；MIT（麻省理工学院）；CBMM（计算生物学与机器学习中心）；Istituto Italiano di Tecnologia（意大利技术研究院）；University of Chicago（芝加哥大学）；Institute of Science and Technology Austria (ISTA)（奥地利科学技术研究所）
Does Location Matter? A Black-Box Causal Audit Reveals Model-Dependent Audio Grounding in LALM Hallucination
Sharif University of Technology（谢里夫理工大学）；Amirkabir University of Technology（阿米尔卡比尔理工大学）；New Uzbekistan University（新乌兹别克斯坦大学）；Okinawa Institute of Science and Technology（冲绳科学技术大学院大学）
