Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions
从多节点干预识别线性混合的因果表示AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要以跨环境干预识别潜在因果变量为核心，直接涉及因果表示与识别；多节点干预本身不足以归为网络与干扰。
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从多节点干预识别线性混合的因果表示
- 研究问题
- 当观测是潜在因果变量的线性混合，且一个环境可同时干预多个节点时，如何利用跨环境信息识别并学习底层因果表示？
- 方法与思路
- 作者依靠跨环境干预覆盖与多样性假设，利用干预在潜在变量方差中留下的痕迹施加特定稀疏性正则，并提出实际学习算法。
- 材料陈述的贡献
- 作者称首次给出允许单环境多节点干预的因果表示可识别性结果，并提供学习算法与支持理论结果的经验性证据。
- 阅读关注点
- 核对线性混合模型、干预覆盖与多样性条件、识别允许的等价变换，以及方差痕迹和稀疏性正则如何对应理论及算法。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核验；摘要未列完整条件、识别精度和实验细节，首创性及一般适用性不能据此确认。
原文摘要
The task of inferring high-level causal variables from low-level observations, commonly referred to as causal representation learning, is fundamentally underconstrained. As such, recent works to address this problem focus on various assumptions that lead to identifiability of the underlying latent causal variables. A large corpus of these preceding approaches consider multi-environment data collected under different interventions on the causal model. What is common to virtually all of these works is the restrictive assumption that in each environment, only a single variable is intervened on. In this work, we relax this assumption and provide the first identifiability result for causal representation learning that allows for multiple variables to be targeted by an intervention within one environment. Our approach hinges on a general assumption on the coverage and diversity of interventions across environments, which also includes the shared assumption of single-node interventions of previous works. The main idea behind our approach is to exploit the trace that interventions leave on the variance of the ground truth causal variables and regularizing for a specific notion of sparsity with respect to this trace. In addition to and inspired by our theoretical contributions, we present a practical algorithm to learn causal representations from multi-node interventional data and provide empirical evidence that validates our identifiability results.