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Causal Integrated Gradients: Scalable Feature Attribution Along Causal Paths
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arXiv AI
Oct 16
Causal Integrated Gradients: Scalable Feature Attribution Along Causal Paths
The paper introduces Causal Integrated Gradients (CIG) and Causal Expected Gradients (CEG), which extend integrated gradients by incorporating known causal relationships among input features. By routing the integration path along a causal graph, the methods decompose each feature’s attribution into direct and mediated effects, enabling scalable feature attribution that aligns with true causal influence. Experiments on synthetic data, simulated single‑cell gene expression, and real interferon‑β stimulation data demonstrate that CIG outperforms existing gradient‑based methods in identifying upstream drivers while maintaining computational efficiency.
arXiv Machine Learning
Jul 7
Causal ASCEND: Scalable Two-tier Causal Discovery on High Dimensional Multi-omics Data
arXiv:2607. 04527v1 Announce Type: cross Abstract: Biological systems exhibit a hierarchical structure, characterised by directed flow from upstream regulators to downstream effects.
arXiv AI
Jul 23
Causal dictionary learning reveals and validates transcription-factor binding features in genomic language models
arXiv:2607. 19618v1 Announce Type: cross Abstract: Genomic language models achieve strong performance across regulatory-genomics tasks, yet what these models internally represent remains opaque, and the field lacks a principled procedure for verifying that an apparent ``concept'' inside a model is real rather than an artifact of sequence composition.
arXiv Machine Learning
Jul 23
Local Causal Structure Learning in the Presence of Latent Variables and Selection Bias
arXiv:2607. 19866v1 Announce Type: new Abstract: Discovering the direct causes and effects of a target variable from observational data is a fundamental problem in causal discovery, with broad applications in domains such as gene regulatory analysis and biomedical research.
arXiv AI
3d ago
From Outcome to Representation: Tracing Reasoning Mechanisms through Integrated Policy Gradient
The paper introduces Integrated Policy Gradient (IPG), a training‑free framework that applies policy‑based attribution to large language model reasoning. IPG backpropagates outcome‑level, non‑differentiable signals through entire inference trajectories, using path integration to isolate outcome‑relevant mediators at the component level. Empirical results show that IPG precisely localizes sparse internal components linked to specific reasoning behaviors and enables modulation of reasoning capabilities.
arXiv AI
Jul 14
CDFM: Towards a General-Purpose Causal Discovery Foundation Model
arXiv:2607. 11508v1 Announce Type: cross Abstract: Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines.