In recent years, proponents of configurational comparative methods (CCMs) have advanced various dimensions of robustness as instrumental to model selection. But these robustness considerations have not led to computable robustness measures, and they have typically been applied to the analysis of real-life data with unknown underlying causal structures, rendering it impossible to determine exactly how they influence the correctness of selected models. This article develops a computable criterion of fit-robustness, which quantifies the degree to which a CCM model agrees with other models inferred from the same data under systematically varied threshold settings of fit parameters. Based on two extended series of inverse search trials on data simulated from known causal structures, the article moreover provides a precise assessment of the degree to which fit-robustness scoring is conducive to finding a correct causal model and how it compares to other approaches of model selection.

近年来，构型比较方法（CCM）的支持者已经将稳健性的各个方面提升为模型选择的工具。但是这些鲁棒性考虑并未导致可计算的鲁棒性度量，它们通常用于具有未知潜在因果结构的现实数据分析，因此无法确切确定它们如何影响所选模型的正确性。本文开发了适合健壮性的可计算标准，它量化了在拟合参数系统地变化的阈值设置下，CCM模型与从相同数据推断出的其他模型的一致程度。此外，基于对从已知因果结构模拟的数据的两个扩展的逆搜索试验系列，文章还提供了对稳健性评分有助于寻找正确因果模型的程度的精确评估，以及它与其他模型方法的比较选择。