arXiv Machine Learning
Jul 15
Falsifying Causal Graphs With Outlier Events
arXiv:2607. 12145v1 Announce Type: cross Abstract: True causal relationships are rarely known, and inferring causal graphs from data is hard.
Actual causality in fault trees
arXiv:2607. 01840v1 Announce Type: new Abstract: Fault trees are a widely used as effective risk models for complex systems, answering the question "what can go wrong?
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arXiv Machine Learning
Jun 19
Unsupervised Causal Abstractions Discovery
arXiv:2606. 19594v1 Announce Type: new Abstract: Causal abstractions formalize when a high-level structural causal model (SCM) captures the interventional behavior of a lower-level SCM.
arXiv Machine Learning
Jul 14
Relaxing Faithfulness with Intervention-Only Causal Discovery
arXiv:2607. 11816v1 Announce Type: new Abstract: Causal discovery algorithms learn a network that describes the causal dependencies among random variables.
arXiv Machine Learning
Jul 2
Root Cause Analysis of Outliers in Unknown Cyclic Graphs
arXiv:2510. 06995v2 Announce Type: replace-cross Abstract: We study the propagation of outliers in cyclic causal graphs with linear structural equations, tracing them back to one or several "root cause" nodes.
arXiv AI
Aug 5
Computing Actual Causes for Neural Network Predictions under Structured Causal Inputs
arXiv:2608. 03772v1 Announce Type: new Abstract: Explaining the predictions of neural networks is a central challenge in trustworthy AI.
arXiv AI
Jun 17
CausalT5k: Diagnosing Refusal and Failure Modes in Trustworthy Causal Reasoning Across Causal Rungs
arXiv:2602. 08939v2 Announce Type: replace Abstract: Large language models increasingly produce fluent causal explanations, yet they often fail in ways aggregate accuracy cannot diagnose: confusing association with intervention, abandoning correct judgments under pressure, over-refusing valid claims, or answering when evidence is underdetermined.
arXiv AI
Jul 28
Ordering-based Causal Discovery via Generalized Score Matching
arXiv:2601. 16249v3 Announce Type: replace-cross Abstract: Learning DAG structures from purely observational data remains a long-standing challenge across scientific domains.
arXiv Machine Learning
Jun 5
StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysis
arXiv:2606. 05636v1 Announce Type: new Abstract: Root-Cause Analysis (RCA) seeks to identify the variables responsible for abnormal system behavior in complex domains such as manufacturing, cloud computing, and healthcare.
Hugging Face Trending Papers
Jun 25
A Causal Foundation Model for Structure and Outcome Prediction
We introduce TabPFN-CFM, a causal foundation model that can handle multiple causal problems. TabPFN-CFM predicts both causal structure and outcomes from observational data, supports queries on all three levels of Pearl's Causal Hierarchy and uses known graph structure when available to improve predictions.
arXiv Machine Learning
Jun 26
A Causal Foundation Model for Structure and Outcome Prediction
arXiv:2606. 26467v1 Announce Type: new Abstract: We introduce TabPFN-CFM, a causal foundation model that can handle multiple causal problems.
arXiv Machine Learning
Jun 29
Estimation--Prediction Tradeoff in Causal Probabilistic Temporal Graphs
arXiv:2606. 28225v1 Announce Type: new Abstract: Temporal link prediction is usually evaluated by predictive performance on unseen edges, but in probabilistic temporal graphs this criterion can conflate model error with irreducible uncertainty.
arXiv Machine Learning
Jul 31
EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis
arXiv:2607. 27290v1 Announce Type: new Abstract: Modern telecommunication, cloud, and microservice systems emit correlated alarm cascades when components fail.