The Causal Roadmap outlines a systematic approach to our research endeavors: define quantity of interest, evaluate needed assumptions, conduct statistical estimation, and carefully interpret of results. At the estimation step, it is essential that the estimation algorithm be chosen thoughtfully for its theoretical properties and expected performance. Simulations can help researchers gain a better understanding of an estimator's statistical performance under conditions unique to the real-data application. This in turn can inform the rigorous pre-specification of a Statistical Analysis Plan (SAP), not only stating the estimand (e.g., G-computation formula), the estimator (e.g., targeted minimum loss-based estimation [TMLE]), and adjustment variables, but also the implementation of the estimator -- including nuisance parameter estimation and approach for variance estimation. Doing so helps ensure valid inference (e.g., 95% confidence intervals with appropriate coverage). Failing to pre-specify estimation can lead to data dredging and inflated Type-I error rates.

翻译：《因果路线图》系统性地规划了研究工作的框架：明确目标量、评估所需假设、实施统计估计、审慎解读结果。在估计阶段，需根据理论性质与预期表现审慎选择估计算法。模拟研究可帮助研究者深入理解在真实数据应用特定条件下估计量的统计性能，从而为统计分析计划（SAP）的严谨预先设定提供依据——内容不仅涵盖目标参量（如G计算方程）、估计量（如基于目标最小损失估计TMLE）及调整变量，更需详述估计器的实施细节，包括干扰参数估计方法与方差估计策略。如此操作方能保障有效推断（例如构建覆盖率达标的95%置信区间），而缺乏预先设定的估计流程将引发数据挖掘与第一类错误概率膨胀。