Causal Decomposition Analysis with Synergistic Interventions: A Triply-Robust Machine Learning Approach to Addressing Multiple Dimensions of Social Disparities

arXiv stat.ML / 4/17/2026

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Key Points

The paper argues that educational disparities driven by race, socioeconomic status, and geography may not be adequately addressed by single-domain interventions, especially for groups facing multiple disadvantages.
It proposes an extended causal decomposition analysis framework that evaluates multiple causally ordered interventions together to quantify their synergistic effects.
To handle practical difficulties such as model misspecification arising from complex interactions among group categories, intermediate factors, and confounders, the authors introduce a “triply robust” estimator using machine learning.
The method is applied to a student cohort from the High School Longitudinal Study to study whether two sequential interventions—improving high-performing school attendance proportions and equalizing Algebra I enrollment by 9th grade—can reduce math achievement gaps among Black, Hispanic, and White students.

Abstract

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.