Enhancing Causal Pursuits in Organizational Science: Targeting the Effect of Treatment on the Treated in Research on Vulnerable Populations
提出在组织科学中研究弱势群体时，使用因果推断中的处理组平均处理效应（ETT）替代传统平均处理效应，因其更易解释、更准确且对假设违背更稳健，并介绍了两种双重稳健估计方法。
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.