The Finite Sample Performance of Estimators for Mediation Analysis Under Sequential Conditional Independence
基于瑞士求职者数据的模拟研究，比较了回归、逆概率加权等参数和半参数估计量在序贯条件独立性假设下估计直接和间接因果效应的有限样本表现，发现“g计算”法整体最优，但不同估计量表现差异不大且随数据生成过程变化。
Using a comprehensive simulation study based on empirical data, this article investigates the finite sample properties of different classes of parametric and semiparametric estimators of (natural) direct and indirect causal effects used in mediation analysis under sequential conditional independence assumptions. The estimators are based on regression, inverse probability weighting, and combinations thereof. Our simulation design uses a large population of Swiss jobseekers and considers variations of several features of the data-generating process (DGP) and the implementation of the estimators that are of practical relevance. We find that no estimator performs uniformly best (in terms of root mean squared error) in all simulations. Overall, so-called “g-computation” dominates. However, differences between estimators are often (but not always) minor in the various setups and the relative performance of the methods often (but not always) varies with the features of the DGP.