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：因果路线图概述了研究的系统性方法：定义目标量、评估所需假设、进行统计估计以及谨慎解释结果。在估计步骤中，必须根据理论特性和预期表现精心选择估计算法。模拟研究有助于研究者更好地理解在真实数据应用特定条件下估计量的统计性能。这进而能为统计分析计划（SAP）的严格预设定提供依据——不仅需明确目标量（例如G计算公式）、估计量（例如基于目标最小损失估计[TMLE]）及调整变量，还需规定估计量的实施细节，包括干扰参数估计和方差估计方法。这样做有助于确保有效的统计推断（例如具有适当覆盖率的95%置信区间）。未能预先设定估计方法可能导致数据挖掘和I类错误率膨胀。