Causal relationships play a pivotal role in research within the field of public administration. Ensuring reliable causal inference requires validating the predictability of these relationships, which is a crucial precondition. However, prediction has not garnered adequate attention within the realm of quantitative research in public administration and the broader social sciences. The advent of interpretable machine learning presents a significant opportunity to integrate prediction into quantitative research conducted in public administration. This article delves into the fundamental principles of interpretable machine learning while also examining its current applications in social science research. Building upon this foundation, the article further expounds upon the implementation process of interpretable machine learning, encompassing key aspects such as dataset construction, model training, model evaluation, and model interpretation. Lastly, the article explores the disciplinary value of interpretable machine learning within the field of public administration, highlighting its potential to enhance the generalization of inference, facilitate the selection of optimal explanations for phenomena, stimulate the construction of theoretical hypotheses, and provide a platform for the translation of knowledge. As a complement to traditional causal inference methods, interpretable machine learning ushers in a new era of credibility in quantitative research within the realm of public administration.

翻译：因果关系在公共管理领域研究中扮演着关键角色。确保可靠的因果推断需要验证这些关系的可预测性，这是一个至关重要的先决条件。然而，预测在公共管理乃至更广泛的社会科学定量研究中尚未获得足够重视。可解释机器学习的出现为将预测整合到公共管理定量研究中提供了重要契机。本文深入探讨了可解释机器学习的基本原理，同时审视了其目前在社会科学研究中的应用。在此基础上，文章进一步阐述了可解释机器学习的实施流程，涵盖数据集构建、模型训练、模型评估与模型解释等关键环节。最后，本文探讨了可解释机器学习在公共管理领域的学科价值，强调其在提升推断的泛化能力、促进为现象选择最佳解释、激发理论假设构建以及为知识转化提供平台方面的潜力。作为对传统因果推断方法的补充，可解释机器学习为公共管理领域的定量研究开启了可信度的新时代。