Understanding the experiences of vulnerable workers is an important scientific pursuit. For example, research interest is often in quantifying the impacts of adverse exposures such as discrimination, exclusion, harassment, or job insecurity, among others. However, routine approaches have only focused on the average treatment effect, which encapsulates the impact of an exposure (e.g., discrimination) applied to the entire study population—including those who were not exposed. In this paper, we propose using a more refined causal quantity uniquely suited to address such causal queries: The effect of treatment on the treated (ETT) from the causal inference literature. We explain why the ETT is a more pertinent causal estimand for investigating the experiences of vulnerable workers by highlighting three appealing features: Better interpretability, greater accuracy, and enhanced robustness to violations of empirically untestable causal assumptions. We further describe how to estimate the ETT by introducing and comparing two estimators. Both estimators are conferred with a so-called doubly robust property. We hope the current proposal empowers organizational scholars in their crucial endeavors dedicated to understanding the vulnerable workforce.

中文翻译：

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加强组织科学中的因果追寻：在弱势群体研究中瞄准治疗对被治疗者的影响

了解弱势工人的经历是一项重要的科学追求。例如，研究兴趣通常是量化歧视、排斥、骚扰或工作不稳定等不利因素的影响。然而，常规方法只关注平均治疗效果，它概括了暴露（例如歧视）对整个研究人群（包括那些未暴露的人群）的影响。在本文中，我们建议使用更精确的因果量，特别适合解决此类因果查询：来自因果推理文献的治疗对被治疗者的影响（ETT）。我们通过强调三个吸引人的特征来解释为什么 ETT 是调查弱势工人经历的更相关的因果估计：更好的可解释性、更高的准确性以及对违反经验上无法检验的因果假设的增强的鲁棒性。我们通过介绍和比较两个估计器进一步描述如何估计 ETT。两个估计量都被赋予了所谓的双鲁棒性。我们希望当前的提案能够赋予组织学者致力于了解弱势劳动力的重要努力。