Consistency and coverage are two core parameters of model fit used by configurational comparative methods (CCMs) of causal inference. Among causal models that perform equally well in other respects (e.g., robustness or compliance with background theories), those with higher consistency and coverage are typically considered preferable. Finding the optimally obtainable consistency and coverage scores for data $\text{δ}$, so far, is a matter of repeatedly applying CCMs to $\text{δ}$ while varying threshold settings. This article introduces a procedure called ConCovOpt that calculates, prior to actual CCM analyses, the consistency and coverage scores that can optimally be obtained by models inferred from $\text{δ}$. Moreover, we show how models reaching optimal scores can be methodically built in case of crisp-set and multi-value data. ConCovOpt is a tool, not for blindly maximizing model fit, but for rendering transparent the space of viable models at optimal fit scores in order to facilitate informed model selection—which, as we demonstrate by various data examples, may have substantive modeling implications.

一致性和覆盖率是因果推理的配置比较方法 (CCM) 使用的模型拟合的两个核心参数。在其他方面（例如，稳健性或与背景理论的合规性）表现同样出色的因果模型中，通常认为具有更高一致性和覆盖率的因果模型更可取。为数据寻找最佳可获得的一致性和覆盖率分数$\text{δ}$，到目前为止，是反复应用 CCM 的问题 $\text{δ}$同时改变阈值设置。本文介绍了一个称为ConCovOpt的过程，该过程在实际 CCM 分析之前计算可以通过从模型推断出的模型最佳获得的一致性和覆盖率分数$\text{δ}$. 此外，我们展示了如何在清晰集和多值数据的情况下有条不紊地构建达到最佳分数的模型。ConCovOpt 是一种工具，不是为了盲目地最大化模型拟合，而是为了在最佳拟合分数下使可行模型的空间变得透明，以促进明智的模型选择——正如我们通过各种数据示例所证明的那样，这可能具有实质性的建模意义。