The statement is correct but misleading. A casual reader would be under the impression that these assumptions and the associated “special-purpose tools” are on the fringes of causal inference. On the contrary, instrumental variable methods are immensely popular in the social sciences. So are regression discontinuity and difference-in-differences designs, which are other methods relying on functional form assumptions (continuity and additivity, respectively). These methods are omitted from Pearl’s account of the causal revolution. If readers were made aware of their existence and popularity, they might question whether “causal diagrams provide a complete and systematic way of finding a solution [to confounding].”

中文翻译：

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测量一致性：模型、方法和应用。

这种说法是正确的，但具有误导性。不经意的读者会认为这些假设和相关的“专用工具”处于因果推理的边缘。相反，工具变量方法在社会科学中非常流行。回归不连续性和差异中的差异设计也是如此，它们是依赖于函数形式假设（分别为连续性和可加性）的其他方法。这些方法在珀尔对因果革命的描述中被省略了。如果让读者意识到它们的存在和流行，他们可能会质疑“因果图是否提供了一种完整和系统的方法来寻找[混淆]的解决方案。”