LoSAM: Local Search in Additive Noise Models with Mixed Mechanisms and General Noise for Global Causal Discovery

2024/10/15 by Sujai Hiremath, Hiremath, Sujai, Promit Ghosal +3 · 2 citations
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Music and Audio Processing #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2410.11759

openalex publication_date 2024/10/15 · openalex created_date 2024/10/20 · openalex updated_date 2026/07/28

Abstract

Inferring causal relationships from observational data is crucial when experiments are costly or infeasible. Additive noise models (ANMs) enable unique directed acyclic graph (DAG) identification, but existing sample-efficient ANM methods often rely on restrictive assumptions on the data generating process, limiting their applicability to real-world settings. We propose local search in additive noise models, LoSAM, a topological ordering method for learning a unique DAG in ANMs with mixed causal mechanisms and general noise distributions. We introduce new causal substructures and criteria for identifying roots and leaves, enabling efficient top-down learning. We prove asymptotic consistency and polynomial runtime, ensuring scalability and sample efficiency. We test LoSAM on synthetic and real-world data, demonstrating state-of-the-art performance across all mixed mechanism settings.

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• When Additive Noise Meets Unobserved Mediators: Bivariate Denoising Diffusion for Causal Discovery
• Optimal Adjustment Sets for Nonparametric Estimation of Weighted Controlled Direct Effect

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