SCMD: A Kernel-Based Distance for Structural Causal Models to Quantify Transferability Across Environments
SCMD:量化跨环境可迁移性的结构因果模型核距离
Out-of-distribution generalization is key to building models that remain reliable across diverse environments. Recent causality-based methods address this challenge by learning invariant causal relationships in the underlying data-generating process. Yet, measuring how causal structures differ across environments, and the resulting generalization difficulty, remains difficult. To tackle this challenge, we propose the Structural Causal Model Distance (SCMD), a principled…