Detecting and Identifying Selection Structure in Sequential Data
序列数据中选择结构的检测与识别AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要核心是选择机制与潜在混杂下的因果结构非参数识别。
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序列数据中选择结构的检测与识别
- 研究问题
- 序列数据因潜在目标而被选择性纳入时，如何从观测依赖中发现选择机制，并在潜在混杂并存时识别其因果结构？
- 方法与思路
- 作者研究选择结构的非参数可识别性，提出检测与识别选择结构及其他依赖类型的算法，并使用合成数据和真实音乐验证。
- 材料陈述的贡献
- 摘要称，无需参数化假设或干预实验即可识别选择结构；在与潜在混杂并存时，适当结构条件下仍可获得非参数识别。
- 阅读关注点
- 应核对选择变量的定义、无干预识别依赖的结构条件、潜在混杂允许范围，以及算法如何区分选择诱导和其他来源的依赖。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未列出适当结构条件，无参数假设不能理解为完全不需要识别假设。
原文摘要
We argue that the selective inclusion of data points based on latent objectives is common in practical situations, such as music sequences. Since this selection process often distorts statistical analysis, previous work primarily views it as a bias to be corrected and proposes various methods to mitigate its effect. However, while controlling this bias is crucial, selection also offers an opportunity to provide a deeper insight into the hidden generation process, as it is a fundamental mechanism underlying what we observe. In particular, overlooking selection in sequential data can lead to an incomplete or overcomplicated inductive bias in modeling, such as assuming a universal autoregressive structure for all dependencies. Therefore, rather than merely viewing it as a bias, we explore the causal structure of selection in sequential data to delve deeper into the complete causal process. Specifically, we show that selection structure is identifiable without any parametric assumptions or interventional experiments. Moreover, even in cases where selection variables coexist with latent confounders, we still establish the nonparametric identifiability under appropriate structural conditions. Meanwhile, we also propose a provably correct algorithm to detect and identify selection structures as well as other types of dependencies. The framework has been validated empirically on both synthetic data and real-world music.