`All models are wrong but some are useful' (George Box 1979). But, how to find those useful ones starting from an imperfect model? How to make informed data-driven decisions equipped with an imperfect model? These fundamental questions appear to be pervasive in virtually all empirical fields -- including economics, finance, marketing, healthcare, climate change, defense planning, and operations research. This article presents a modern approach (builds on two core ideas: abductive thinking and density-sharpening principle) and practical guidelines to tackle these issues in a systematic manner.

翻译：“所有模型都是错误的，但有些是有用的”（George Box，1979）。然而，如何从存在缺陷的模型出发找到那些有用的模型？如何借助不完美的模型做出基于数据的知情决策？这些基本问题似乎普遍存在于几乎所有实证领域——包括经济学、金融学、市场营销、医疗健康、气候变化、国防规划与运筹学。本文提出了一种现代方法（建立在溯因推理与密度锐化原则两大核心思想之上）及系统化应对这些问题的实践指南。