Newest papers

Causal Integrated Gradients: Scalable Feature Attribution Along Causal Paths

Joran Michiels, Anis Ismail, Maarten De Vos, Alejandro Sifrim · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.22126 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

现有的基于基因特征驱动的单个特征分配方法是数据特征依赖的数据不会扩展到高维度。我们提出因果综合分配(CIG)和因果预期分配(CEG),这些方法扩展了综合分配,以便在输入特征中包含先前因果结构,同时保持其计算效率,在因果图的条件下,其无定义保证。在许多设置中,因果之间的某些因果结构已经知道,例如,一个因果分配因子因果调节了目标基因表达的因果结构。然而,现有的基因分配方法无法利用此结构,相反地将因果的下游效应归咎于其真实因果上,如对该因子调节的目标基因的排名。 我们的关键想法是将这种因果结构

Machine translation (Meta M2M100); the English original follows.

Abstract

Existing feature attribution methods that account for feature dependencies do not scale to high dimensions. We propose causal integrated gradients (CIG) and causal expected gradients (CEG), which extend integrated gradients to incorporate prior causal structure among input features while retaining its computational efficiency and, under conditions on the causal graph, its axiomatic guarantees. In many settings, some causal structure between features is already known, for example, that a transcription factor causally regulates a target gene's expression. Yet existing gradient-based methods cannot exploit this structure, instead crediting a feature's downstream effects over its true upstream cause, such as ranking the target gene above the transcription factor that regulates it. Our key idea is to embed this causal structure by routing the integration path along the causal graph. The method thereby decomposes each feature's attribution into a direct effect and effects mediated through its causal descendants. We evaluate CIG in three settings of increasing realism. First, on a synthetic benchmark with a known causal model, CIG's attributions best match the ground-truth causal effect from Pearl's do-calculus, matching the accuracy of causal Shapley values at a fraction of the computational cost. Second, on simulated single-cell gene expression data with a known gene regulatory network (Dyngen), CIG most accurately recovers the transcription factors driving the simulation. Third, on real interferon-$β$ stimulation data, where only an approximate causal graph is available, CIG better prioritizes the transcription factors driving the measured response than DeepSHAP and integrated gradients. Together, these results show that incorporating causal structure into gradient-based attributions recovers upstream drivers that existing methods miss, without sacrificing scalability.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Computer science, Causal model, Causal structure, Attribution, Scalability, Artificial intelligence

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange