LoSAM: Local Search in Additive Noise Models with Unmeasured Confounders, a Top-Down Global Discovery Approach

• URL: http://arxiv.org/abs/2410.11759v3
• Date: Sun, 10 Nov 2024 19:03:35 GMT
• Title: LoSAM: Local Search in Additive Noise Models with Unmeasured Confounders, a Top-Down Global Discovery Approach
• Authors: Sujai Hiremath, Promit Ghosal, Kyra Gan,
• Abstract summary: We introduce local search in additive noise model (LoSAM) LoSAM generalizes an existing nonlinear method that leverages local causal substructures to the general additive noise setting. We show that LoSAM achieves runtime, and improves runtime and efficiency by exploiting new substructures.
• Score: 2.4305626489408465
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We address the challenge of causal discovery in structural equation models with additive noise without imposing additional assumptions on the underlying data-generating process. We introduce local search in additive noise model (LoSAM), which generalizes an existing nonlinear method that leverages local causal substructures to the general additive noise setting, allowing for both linear and nonlinear causal mechanisms. We show that LoSAM achieves polynomial runtime, and improves runtime and efficiency by exploiting new substructures to minimize the conditioning set at each step. Further, we introduce a variant of LoSAM, LoSAM-UC, that is robust to unmeasured confounding among roots, a property that is often not satisfied by functional-causal-model-based methods. We numerically demonstrate the utility of LoSAM, showing that it outperforms existing benchmarks.

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