分层结构因果模型的可扩展与通用识别：对STAR项目的新视角
Scalable and Versatile Identification for Hierarchical Structural Causal Models: A New Look at Project STAR
AI总结：
针对分层结构因果模型，本文开发了一套连接符号识别与实际估计的可扩展开源管道，经验证后应用于STAR项目，发现扁平基线无法编码班级干预，符号识别需结合可扩展估计与数值稳定性检查。
AI中文摘要：
STAR（师生成就比）实验（1985年，美国田纳西州）是一个具有里程碑意义的分层数据集，旨在评估班级规模对学生成绩的影响，其观测值嵌套在班级中。为了在这类分层场景中编码班级层面的干预措施，我们开发了一套完整、可扩展、开源的分层结构因果模型（HSCM）管道，该管道连接了符号识别与实际估计。我们的方法整合了图变换、pyAgrum的do演算（用于自动识别因果效应）、符号表达式向闭式HSCM公式的适配，以及从拟合的局部概率模型进行数值估计。一项关键创新是我们适配的抽象语法树（AST），它将pyAgrum识别出的公式分解为独立的密度、期望和边缘化任务，从而实现并行且可扩展的计算。我们在具有已知真实值的典型HSCM基序和基准场景上验证了该管道，随后将其应用于STAR项目的幼儿园数学成绩。结果表明，忽略分层结构的扁平基线能够恢复关联，但无法编码班级层面的干预措施；仅靠符号识别不足以用于实际的分层结构因果推断，可扩展估计和数值稳定性检查是科学目标的核心组成部分。
英文摘要：
The STAR (Student-Teacher Achievement Ratio) experiment (1985, Tennessee, USA) is a landmark hierarchical dataset designed to assess the impact of class size on student outcomes, with observations nested within classes. To encode class-level interventions in such hierarchical settings, we develop a complete, scalable, open-source pipeline for Hierarchical Structural Causal Models (HSCM) that bridges symbolic identification and practical estimation. Our approach integrates graph transformations, pyAgrum's do-calculus for automatic identification of causal effects, adaptation of symbolic expression into closed-form HSCM formulas, and numerical estimation from fitted local probability models. A key innovation is our adapted Abstract Syntax Tree (AST), which decomposes pyAgrum's identified formulas into independent density, expectation, and marginalization tasks, enabling parallel and scalable computation. We validate the pipeline on canonical HSCM motifs and benchmark scenarios with known ground truth, then apply it to STAR kindergarten mathematics outcomes. The results show that flat baselines (ignoring hierarchy) recover associations but fail to encode class-level interventions, and that symbolic identification alone is not enough for practical Hierarchical Structural Causal inference; scalable estimation and numerical stability checks are central parts of the scientific object.
