Educational disparities are rooted in and perpetuate social inequalities across multiple dimensions such as race, socioeconomic status, and geography. To reduce disparities, most intervention strategies focus on a single domain and frequently evaluate their effectiveness by using causal decomposition analysis. However, a growing body of research suggests that single-domain interventions may be insufficient for individuals marginalized on multiple fronts. While interventions across multiple domains are increasingly proposed, there is limited guidance on appropriate methods for evaluating their effectiveness. To address this gap, we develop an extended causal decomposition analysis that simultaneously targets multiple causally ordered intervening factors, allowing for the assessment of their synergistic effects. These scenarios often involve challenges related to model misspecification due to complex interactions among group categories, intervening factors, and their confounders with the outcome. To mitigate these challenges, we introduce a triply robust estimator that leverages machine learning techniques to address potential model misspecification. We apply our method to a cohort of students from the High School Longitudinal Study, focusing on math achievement disparities between Black, Hispanic, and White high schoolers. Specifically, we examine how two sequential interventions - equalizing the proportion of students who attend high-performing schools and equalizing enrollment in Algebra I by 9th grade across racial groups - may reduce these disparities.

翻译：教育不平等植根于并延续了种族、社会经济地位及地理分布等多重维度的社会不公。为缩小差距，多数干预策略聚焦单一领域，并常通过因果分解分析评估其成效。然而，日益增多的研究表明，对多重边缘化个体而言，单一领域干预可能收效甚微。尽管跨领域干预方案正被广泛提出，但评估其有效性的适宜方法论指导仍十分有限。为填补这一空白，我们发展了扩展性因果分解分析方法，可同步针对多个具有因果时序的中介因素开展研究，从而评估其协同效应。此类场景常面临模型误设挑战——源于群体类别、中介因素及其与结局变量间混杂因素的复杂交互作用。为缓解该问题，我们引入三重稳健估计量，借助机器学习技术应对潜在模型误设。我们将该方法应用于高中纵向研究的学生队列，聚焦黑裔、西裔与白裔高中生数学成就差异。具体而言，我们考察两类序贯干预措施——提高不同种族学生入读优质高中的比例、均衡九年级代数I课程的修读率——对缩小上述差异的潜在效果。