The causal inference methods of potential outcomes (POs), directed acyclic graphs (DAGs), and structural equation models (SEMs) have contributed much to our understanding of causal effects. Yet the teaching and application of these methods (especially POs and DAGs) have nearly always regarded treatment as binary even when the magnitude of treatment can differ greatly. The two most common types of binary treatments are those from randomized experiments and those that are categorized versions of continuous treatments. Binary treatments via categorization are far more common in observational studies. I derive results showing that binary treatment variables that have different origins should be treated differently. Not doing so makes biased causal inferences more likely. I illustrate the value of combining POs, DAGs, and SEMs perspectives to illuminate potential problems with binary treatments rather than relying only on one perspective. The new analytic results are illustrated with simulations and an empirical example. Finally, I make recommendations on how researchers should analyze binary treatments. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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使用随机化的二元处理与分类的二元处理进行因果推断。

潜在结果（PO）、有向无环图（DAG）和结构方程模型（SEM）的因果推理方法对我们对因果效应的理解做出了很大贡献。然而，这些方法（尤其是 PO 和 DAG）的教学和应用几乎总是将治疗视为二元的，即使治疗的程度可能相差很大。两种最常见的二元治疗类型是来自随机实验的治疗和连续治疗的分类版本。通过分类进行二元处理在观察性研究中更为常见。我得出的结果表明，具有不同来源的二元处理变量应该得到不同的处理。不这样做就会更有可能产生有偏见的因果推论。我阐述了结合 PO、DAG 和 SEM 观点来阐明二元处理的潜在问题的价值，而不是仅依赖一种观点。新的分析结果通过模拟和实证示例进行了说明。最后，我就研究人员应如何分析二元治疗提出建议。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。