This paper aims to broaden the understanding of quasi-experimental methods among marketing scholars and those who read their work by describing the underlying logic and set of actions that make their work convincing. The purpose of quasi-experimental methods is, in the absence of experimental variation, to determine the presence of a causal relationship. First, the paper explores how to identify settings and data where it is interesting to understand whether an action causally affects a marketing outcome. Second, the paper outlines how to structure an empirical strategy that allows the author to identify a causal empirical relationship. The paper details the application of various methods to identify how an action affects an outcome in marketing, including difference-in-differences, regression discontinuity, instrumental variables, propensity score matching, synthetic control, and selection bias correction. The paper emphasizes the importance of clearly communicating the identifying assumptions underlying the assertion of causality. Last, the paper explains how exploring the behavioral mechanism—whether individual, organizational, or market-level— can actually reinforce arguments of causality.

本文旨在通过描述使他们的工作令人信服的基本逻辑和一系列行动，扩大营销学者和阅读他们作品的人对准实验方法的理解。准实验方法的目的是在没有实验变化的情况下确定因果关系的存在。首先，本文探讨了如何识别有趣的设置和数据，以了解一个行为是否会影响营销结果。其次，本文概述了如何构建允许作者识别因果经验关系的经验策略。本文详细介绍了各种方法的应用，以识别行动如何影响营销结果，包括差异中的差异、回归不连续性、工具变量、倾向得分匹配、综合控制和选择偏差校正。该论文强调了清楚地传达因果关系断言背后的识别假设的重要性。最后，本文解释了探索行为机制——无论是个人、组织还是市场层面——实际上可以加强因果关系的论点。