This note illustrates, using simple examples, how causal questions of non-trivial character can be represented, analyzed and solved using linear analysis and path diagrams. By producing closed form solutions, linear analysis allows for swift assessment of how various features of the model impact the questions under investigation. We discuss conditions for identifying total and direct effects, representation and identification of counterfactual expressions, robustness to model misspecification, and generalization across populations.

中文翻译：

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用于干预和反事实的线性“显微镜”

本笔记通过简单的例子说明了如何使用线性分析和路径图来表示、分析和解决非平凡特征的因果问题。通过生成封闭形式的解决方案，线性分析允许快速评估模型的各种特征如何影响正在调查的问题。我们讨论了识别总体和直接影响的条件、反事实表达的表示和识别、模型错误规范的鲁棒性以及跨群体的泛化。