Causal Decomposition Analysis with Synergistic Interventions: A Triply-Robust Machine Learning Approach to Addressing Multiple Dimensions of Social Disparities
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协同干预的因果分解分析：处理多维社会差距的三重稳健机器学习方法
- 研究问题
- 当社会差距涉及多个按因果顺序排列的干预因素时，如何评价联合干预的协同作用及其缩小教育差距的潜力？
- 方法与思路
- 作者扩展因果分解分析以同时干预多个有序因素，提出结合机器学习的三重稳健估计量，并分析学校与代数课程干预。
- 材料陈述的贡献
- 摘要称提供评价多领域协同干预的因果分解方法，以三重稳健估计应对模型误设，并应用于高中生数学成绩差距。
- 阅读关注点
- 应核对联合干预和协同效应的定义、时序混杂的处理、三重稳健成立的模型组合，以及机器学习推断条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明完整识别假设，也未报告两项干预降低差距的数值结果。
原文摘要
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.