Towards Identifiable Latent Additive Noise Models
迈向可识别的潜在加性噪声模型AI 解读 ↓
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迈向可识别的潜在加性噪声模型
- 研究问题
- 能否放宽因果表示学习对模型形式及环境变化的限制，在仅部分潜在因果影响发生变化时仍识别潜在因果结构？
- 方法与思路
- 作者通过约束函数类别建立潜在加性噪声模型框架，推导部分识别结果，再扩展至潜在后非线性模型并构造学习方法。
- 材料陈述的贡献
- 摘要称在更弱条件及部分因果影响变化的情形下获得部分识别，并用合成、半合成数据和人体运动案例展示方法表现。
- 阅读关注点
- 应核对函数类别约束、变化机制与可识别对象，尤其区分部分识别保留的歧义，以及后非线性扩展需要的额外条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未列出完整识别条件，也未说明真实案例能否验证潜在因果结构。
原文摘要
Causal representation learning (CRL) offers the promise of uncovering the underlying causal model by which observed data was generated, but the practical applicability of existing methods remains limited by the strong assumptions required for identifiability and by challenges in applying them to real-world settings. Most current approaches are applicable only to relatively restrictive model classes, such as linear or polynomial models, which limits their flexibility and robustness in practice. One promising approach to this problem seeks to address these issues by leveraging changes in causal influences among latent variables. In this vein we propose a more general and relaxed framework than typically applied, formulated by imposing constraints on the function classes applied. Within this framework, we establish partial identifiability results under weaker conditions, including scenarios where only a subset of causal influences change. We then extend our analysis to a broader class of latent post-nonlinear models. Building on these theoretical insights, we develop a flexible method for learning latent causal representations. We demonstrate the effectiveness of our approach on synthetic and semi-synthetic datasets, and further showcase its applicability in a case study on human motion analysis, a complex real-world domain that also highlights the potential to broaden the practical reach of identifiable CRL models.