论文标题
定量探测：使用定量域知识验证因果模型
Quantitative probing: Validating causal models using quantitative domain knowledge
论文作者
论文摘要
我们将定量探测作为一种模型不合时宜的框架，用于在存在定量域知识的情况下验证因果模型。该方法被构造为基于相关的机器学习中火车/测试拆分的类似物，并增强了与科学发现逻辑一致的当前因果验证策略。在进行彻底基于模拟的研究之前，使用Pearl的洒水示例说明了该方法的有效性。通过研究示例性失败方案来识别该技术的限制，此外，这些方案还用于提出一个主题列表，以供未来的研究和改进定量探测的版本。在两个单独的开源python软件包中提供了将定量探测纳入因果分析中的代码，以及基于模拟的定量探测有效性的代码。
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.
