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?
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2607. 12145v1 Announce Type: cross Abstract: True causal relationships are rarely known, and inferring causal graphs from data is hard.
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:2607. 11816v1 Announce Type: new Abstract: Causal discovery algorithms learn a network that describes the causal dependencies among random variables.
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:2608. 03772v1 Announce Type: new Abstract: Explaining the predictions of neural networks is a central challenge in trustworthy AI.
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.