Disparities in health or well-being experienced by minority groups can be difficult to study using the traditional exposure-outcome paradigm in causal inference, since potential outcomes in variables such as race or sexual minority status are challenging to interpret. Causal decomposition analysis addresses this gap by positing causal effects on disparities under interventions to other, intervenable exposures that may play a mediating role in the disparity. While invoking weaker assumptions than causal mediation approaches, decomposition analyses are often conducted in observational settings and require uncheckable assumptions that eliminate unmeasured confounders. Leveraging the marginal sensitivity model, we develop a sensitivity analysis for weighted causal decomposition estimators and use the percentile bootstrap to construct valid confidence intervals for causal effects on disparities. We also propose a two-parameter amplification that enhances interpretability and facilitates an intuitive understanding of the plausibility of unmeasured confounders and their effects. We illustrate our framework on a study examining the effect of parental acceptance on disparities in suicidal ideation among sexual minority youth. We find that the effect is small and sensitive to unmeasured confounding, suggesting that further screening studies are needed to identify mitigating interventions in this vulnerable population.

翻译：少数群体在健康或福祉方面经历的差异，难以通过因果推断中传统的暴露-结果范式进行研究，因为诸如种族或性少数群体身份等变量的潜在结果难以解释。因果分解分析通过假设在其他可干预暴露（可能在差异中起中介作用）的干预下对差异的因果效应，来弥补这一不足。虽然分解分析比因果中介方法假设更弱，但通常在观察性环境中进行，并且需要消除未测量混杂因子的不可检验假设。利用边际敏感性模型，我们为加权因果分解估计量开发了一种敏感性分析，并使用百分位数自助法为差异的因果效应构建有效的置信区间。我们还提出了一种双参数放大方法，以增强可解释性并促进对未测量混杂因子及其效应的合理性进行直观理解。我们通过一项研究来说明我们的框架，该研究考察了父母接纳对性少数青年自杀意念差异的影响。我们发现该效应较小且对未测量混杂敏感，这表明需要进一步的筛查研究来识别这一脆弱人群的缓解干预措施。