We present quantitative probing as a model-agnostic framework for validating causal models in the presence of quantitative domain knowledge. The method is constructed as an analogue of the train/test split in correlation-based machine learning and as an enhancement of current causal validation strategies that are consistent with the logic of scientific discovery. The effectiveness of the method is illustrated using Pearl's sprinkler example, before a thorough simulation-based investigation is conducted. Limits of the technique are identified by studying exemplary failing scenarios, which are furthermore used to propose a list of topics for future research and improvements of the presented version of quantitative probing. The code for integrating quantitative probing into causal analysis, as well as the code for the presented simulation-based studies of the effectiveness of quantitative probing is provided in two separate open-source Python packages.

翻译：我们提出定量检验,作为在量化领域知识的情况下验证因果模型的模型 -- -- 不可知性框架,该方法是作为在基于关联的机器学习中进行分解的火车/试验的模拟,作为加强当前符合科学发现逻辑的因果验证战略的一种方法,在进行彻底的模拟调查之前,以Pearl喷洒器为例说明该方法的有效性,通过研究示范性的失败假设方案确定技术的局限性,这些假设方案还被用来提出供今后研究的专题清单,并改进所提出的定量检验方案。 将定量检验纳入因果关系分析的守则,以及关于定量检验有效性的模拟研究的守则,在两个独立的开放源的Python软件包中提供。