论文标题
多个介体的异质介入间接效应：非参数和半参数方法
Heterogeneous interventional indirect effects with multiple mediators: non-parametric and semi-parametric approaches
论文作者
论文摘要
我们提出了半学方法和非参数方法，以在两个离散介体的设置中估计有条件的介入效应，这些离散介体的因果有序尚不清楚。已经证明平均介入间接效应将平均治疗效应分解为直接效应和介入间接效应，从而量化了假设干预对介体分布的影响。然而，这些影响在整个协变量分布中可能是异质的。我们考虑在特定点估算这些效果的问题。我们提出了基于影响功能功能的基于条件效应在工作模型上的投影的估计值，并在某些条件下表明我们可以实现root-n一致和渐近正常估计。其次，我们提出了一种完全非参数的估计方法，并显示了这种方法可以达到甲骨文收敛速率的条件。最后，我们提出了在存在调解人结果混淆的情况下对条件效应的灵敏度分析。我们提出了使用这些相同方法对条件效应的估计界限，并表明这些结果很容易扩展，以允许基于影响 - 函数对平均效应的界限进行估计。我们得出结论，研究了2021年2月在COVID-19的疫苗接种对抑郁症的影响方面的异质作用。
We propose semi- and non-parametric methods to estimate conditional interventional effects in the setting of two discrete mediators whose causal ordering is unknown. Average interventional indirect effects have been shown to decompose an average treatment effect into a direct effect and interventional indirect effects that quantify effects of hypothetical interventions on mediator distributions. Yet these effects may be heterogeneous across the covariate distribution. We consider the problem of estimating these effects at particular points. We propose an influence-function based estimator of the projection of the conditional effects onto a working model, and show under some conditions that we can achieve root-n consistent and asymptotically normal estimates. Second, we propose a fully non-parametric approach to estimation and show the conditions where this approach can achieve oracle rates of convergence. Finally, we propose a sensitivity analysis for the conditional effects in the presence of mediator-outcome confounding. We propose estimating bounds on the conditional effects using these same methods, and show that these results easily extend to allow for influence-function based estimates of the bounds on the average effects. We conclude examining heterogeneous effects with respect to the effect of COVID-19 vaccinations on depression during February 2021.
