标题： 一项广泛的模拟研究，评估多种因果发现背景下重采样技术的相互作用 显示英文标题

标题： An extensive simulation study evaluating the interaction of resampling techniques across multiple causal discovery contexts

摘要： 尽管探索性因果分析在现代科学和医学中的应用日益增多，但用于验证因果模型的现有非实验方法尚未得到充分表征。 其中最流行的方法之一是评估数据重采样后模型特征的稳定性，类似于统计学中用于估计置信区间的重采样方法。 然而，这种方法的许多方面几乎没有受到关注，例如重采样方法的选择是否应取决于样本量、所使用的算法或算法调参参数。 我们提出了理论结果，证明某些重采样方法与为算法调参参数赋值密切相关。 我们还报告了广泛的模拟实验结果，这些结果验证了理论结果，并提供了大量数据，以帮助研究人员进一步表征因果发现分析中的重采样方法。 结合理论工作和模拟结果，为实际应用中如何选择重采样方法和调参参数提供了具体的指导。 显示英文摘要

摘要： Despite the accelerating presence of exploratory causal analysis in modern science and medicine, the available non-experimental methods for validating causal models are not well characterized. One of the most popular methods is to evaluate the stability of model features after resampling the data, similar to resampling methods for estimating confidence intervals in statistics. Many aspects of this approach have received little to no attention, however, such as whether the choice of resampling method should depend on the sample size, algorithms being used, or algorithm tuning parameters. We present theoretical results proving that certain resampling methods closely emulate the assignment of specific values to algorithm tuning parameters. We also report the results of extensive simulation experiments, which verify the theoretical result and provide substantial data to aid researchers in further characterizing resampling in the context of causal discovery analysis. Together, the theoretical work and simulation results provide specific guidance on how resampling methods and tuning parameters should be selected in practice.