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<p>Identifying the causes of social inequality is crucial for designing effective policy interventions. Experimental methods, in which variables are manipulated and participants are randomly assigned to conditions, are widely regarded as the gold standard for causal inference. However, it seems impossible to randomly assign social categories such as gender or race. Does that mean that, for example, gender cannot cause outcomes? We propose a&#xa0;conceptually grounded strategy for the causal treatment of such complex social categories. Gender should be viewed as a&#xa0;multidimensional construct that shapes inequality through different mechanisms. To understand these mechanisms, we can decompose gender into its components (such as perceived social roles or personality traits) and study how each one affects outcomes such as hiring decisions. By manipulating these components in experiments, researchers can identify which aspects of gender actually produce unequal treatment and why. Although the approach itself is not novel per&#xa0;se, its implications are, in our opinion, often insufficiently discussed or theoretically integrated in studies that advance causal claims. We illustrate the utility of this approach through studies of gendered hiring discrimination, showing how gender can be theoretically decomposed into distinct components, which in turn can be operationalized in experimental designs to uncover underlying mechanisms. Beyond gender, the approach applies to other seemingly nonmanipulable characteristics such as race and class, providing a&#xa0;generalizable strategy for mechanism-based causal inference and more targeted policy interventions aimed at reducing social inequality.</p>

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Rethinking Gender and Other Seemingly Nonmanipulable Characteristics for Causal Analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月24日

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  • Isabel M. Habicht,
  • Daria Tisch

摘要

Identifying the causes of social inequality is crucial for designing effective policy interventions. Experimental methods, in which variables are manipulated and participants are randomly assigned to conditions, are widely regarded as the gold standard for causal inference. However, it seems impossible to randomly assign social categories such as gender or race. Does that mean that, for example, gender cannot cause outcomes? We propose a conceptually grounded strategy for the causal treatment of such complex social categories. Gender should be viewed as a multidimensional construct that shapes inequality through different mechanisms. To understand these mechanisms, we can decompose gender into its components (such as perceived social roles or personality traits) and study how each one affects outcomes such as hiring decisions. By manipulating these components in experiments, researchers can identify which aspects of gender actually produce unequal treatment and why. Although the approach itself is not novel per se, its implications are, in our opinion, often insufficiently discussed or theoretically integrated in studies that advance causal claims. We illustrate the utility of this approach through studies of gendered hiring discrimination, showing how gender can be theoretically decomposed into distinct components, which in turn can be operationalized in experimental designs to uncover underlying mechanisms. Beyond gender, the approach applies to other seemingly nonmanipulable characteristics such as race and class, providing a generalizable strategy for mechanism-based causal inference and more targeted policy interventions aimed at reducing social inequality.