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.
By Joran Michiels, Anis Ismail, Maarten De Vos, Alejandro Sifrim
arXiv:2607. 04527v1 Announce Type: cross Abstract: Biological systems exhibit a hierarchical structure, characterised by directed flow from upstream regulators to downstream effects.
By Stephen Asiedu, David Watson
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.
By Sarwan Ali
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.
By Zheng Li, Hao Zhang, Ruxin Wang, Ruichu Cai, Kun Zhang, Feng Xie
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.
By Changming Li, Kaixing Zhang, Haoyun Xu, Yingdong Shi, Zheng Zhang, Kaitao Song, Kan Ren
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.
By Jie Qiao, Ruichu Cai, Zijian Li, Weilin Chen, Pengfei Hua, Boyan Xu, Zhengming Chen, Zhifeng Hao, Peng Cui
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. Existing causal discovery methods either learn a global causal structure, which incurs substantial computational cost, or assume the absence of latent variables and selection bias, assumptions that are often violated in real-world settings.
arXiv:2606. 17516v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions.
By Patrick Bl\"obaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan
arXiv:2606. 03885v1 Announce Type: new Abstract: Feature attribution methods explain predictions by assigning importance scores to input features.
By Kieran A. Murphy, Shameen Shrestha
arXiv:2509. 01916v2 Announce Type: replace Abstract: Causal disentanglement from soft interventions is identifiable under the assumptions of linear interventional faithfulness and availability of both observational and interventional data.
By Jifan Zhang, Michelle M. Li, Elena Zheleva
arXiv:2606. 01042v1 Announce Type: cross Abstract: Perturbation experiments are central to understanding cellular mechanisms, but remain costly and sparse, motivating prediction of gene expression responses for unobserved conditions.
By Xinyu Yuan, Xixian Liu, Jianan Zhao, Yashi Zhang, Hongyu Guo, Jian Tang
arXiv:2609.06941v1 Announce Type: new
Abstract: Causal effect estimation asks how an outcome would change under an intervention, and medicine, economics, and public policy all treat it as a foundatio...
By Haohao Zhou