Enhancing Causal Pursuits in Organizational Science: Targeting the Effect of Treatment on the Treated in Research on Vulnerable Populations

Abstract

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.