Causal decomposition analysis (CDA) is an approach for modeling the impact of hypothetical interventions to reduce disparities. It is useful for identifying foci that future interventions, including multilevel and multimodal interventions, could focus on to reduce disparities. Based within the potential outcomes framework, CDA has a causal interpretation when the identifying assumptions are met. CDA also allows an analyst to consider which covariates are allowable (i.e., fair) for defining the disparity in the outcome and in the point of intervention, so that its interpretation is also meaningful. While the incorporation of causal inference and allowability promotes robustness, transparency, and dialogue in disparities research, it can lead to challenges in estimation such as the need to correctly model densities. Also, how CDA differs from commonly used estimators may not be clear, which may limit its uptake. To address these challenges, we provide a tour of estimation strategies for CDA, reviewing existing proposals and introducing novel estimators that overcome key estimation challenges. Among them we introduce what we call "bridging" estimators that avoid directly modeling any density, and weighted sequential regression estimators that are multiply robust. Additionally, we provide diagnostics to assess the quality of the nuisance density models and weighting functions they rely on. We formally establish the estimators' robustness to model mis-specification, demonstrate their performance through a simulation study based on real data, and apply them to study disparities in hypertension control using electronic health records in a large healthcare system.

翻译：因果分解分析（CDA）是一种用于模拟假设性干预措施对减少差异影响的建模方法。该方法有助于识别未来干预（包括多层次和多模式干预）可聚焦的关键领域以缩小差异。基于潜在结果框架，当识别假设成立时，CDA具有因果解释力。CDA还允许分析者考量哪些协变量在定义结果差异和干预节点时是"允许的"（即公平的），从而确保其解释具有实际意义。尽管因果推断与允许性规范的结合提升了差异研究的稳健性、透明度和讨论空间，但这也带来了估计层面的挑战，例如需要正确建模密度函数。此外，CDA与常用估计器的差异可能不够明确，这限制了其推广应用。为应对这些挑战，本文系统梳理了CDA的估计策略，回顾现有方案并引入能克服关键估计难题的新型估计器。我们提出了避免直接建模任何密度函数的"桥接"估计器，以及具备多重稳健性的加权序贯回归估计器。同时，我们提供了诊断工具以评估其所依赖的辅助密度模型与加权函数的质量。通过理论论证建立了估计器对模型误设的稳健性，基于真实数据的模拟研究验证了其性能，并应用于大型医疗系统的电子健康记录数据以研究高血压控制中的差异现象。