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arXiv AI By Joran Michiels, Anis Ismail, Maarten De Vos, Alejandro Sifrim

Causal Integrated Gradients: Scalable Feature Attribution Along Causal Paths

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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