用因果特征学习方法，从社会数据中提炼更合理的宏观变量。
Causal Feature Learning in the Social Sciences
- 将年龄、收入等属性视为可操控特征的抽象，保持因果推断的模块性。
- 在多个社科数据集上验证，宏观状态比传统微观状态更优。
- 适合做社会学、政策分析的因果建模研究者阅读。
变量选择在因果建模中是个重大挑战，尤其在社会科学领域，许多概念依赖于年龄、社会经济地位、性别和种族等相互关联的因素。有观点认为，这些属性应被建模为底层可操控特征的宏观抽象，以维持因果推断所必需的模块性假设。本文据此扩展了因果特征学习（Causal Feature Learning, CFL）的理论框架。实证上，我们将CFL算法应用于多个社会科学数据集，评估由CFL生成的宏观状态在下游建模任务中的表现，并与传统微观状态进行对比。
原文摘要 · Abstract (English)
Variable selection poses a significant challenge in causal modeling, particularly within the social sciences, where constructs often rely on inter-related factors such as age, socioeconomic status, gender, and race. Indeed, it has been argued that such attributes must be modeled as macro-level abstractions of lower-level manipulable features, in order to preserve the modularity assumption essential to causal inference. This paper accordingly extends the theoretical framework of Causal Feature Learning (CFL). Empirically, we apply the CFL algorithm to diverse social science datasets, evaluating how CFL-derived macrostates compare with traditional microstates in downstream modeling tasks.
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