标题： 解释还是预测？ 显示英文标题

标题： To Explain or to Predict?

摘要： 统计建模是一种通过因果解释、预测和描述来发展和检验理论的强大工具。 在许多学科中，统计建模几乎完全用于因果解释，并假定具有高解释力的模型本质上也具有高预测力。 解释与预测之间的混淆很常见，但为了推动科学知识的进步，必须理解二者的区别。 虽然科学哲学领域已经认识到这一区别，但统计学文献中缺乏对解释性建模与预测性建模建模过程中产生的诸多差异的深入讨论。 本文的目的在于阐明解释性建模与预测性建模的区别，探讨其根源，并揭示该区别对建模过程每一步的实际影响。 显示英文摘要

摘要： Statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and description. In many disciplines there is near-exclusive use of statistical modeling for causal explanation and the assumption that models with high explanatory power are inherently of high predictive power. Conflation between explanation and prediction is common, yet the distinction must be understood for progressing scientific knowledge. While this distinction has been recognized in the philosophy of science, the statistical literature lacks a thorough discussion of the many differences that arise in the process of modeling for an explanatory versus a predictive goal. The purpose of this article is to clarify the distinction between explanatory and predictive modeling, to discuss its sources, and to reveal the practical implications of the distinction to each step in the modeling process.