Original
Mapping the temporal evolution of causal effects in public administration and policy research
Abstract
Abstract Recent growth in the use of randomized and quasi-experiments in public administration and policy research has advanced the ability to establish cause-and-effect relationships. However, many studies adopt static conceptions of causality, focusing on snapshots or time-averaged effects while overlooking how the effects change over time. This oversight is problematic, as interventions of scholarly interest, such as leadership training or the adoption of new technologies, are likely to produce impacts that unfold in various ways. In this paper, we propose a conceptual framework for understanding the temporal dynamics of causal effects and their implications for research hypotheses and design. We then introduce Bayesian changepoint models (BCMs) as a methodological tool for detecting shifts in the average, variance, or trend of causal effect series, providing a more rigorous yet accessible alternative to visually inspecting graphs. Next, we demonstrate the application of BCMs with an illustrative example derived from simulation data as well as a real-world case examining the effect of body-worn cameras on police officers’ use of force. Finally, we discuss how examining temporal changes in effects can advance theoretical understanding of why and how they occur, as well as inform the design and implementation of policies and strategies in practice.
中文
公共行政与政策研究中因果效应的时序演变图谱
摘要
近年来，随机实验与准实验在公共行政与政策研究中的使用日益增多，提升了确立因果关系的能力。然而，许多研究采用静态的因果观，关注某一时点的快照或时间平均效应，而忽视了效应如何随时间变化。这种忽视是有问题的，因为诸如领导力培训或新技术采用等学界关注的干预，很可能产生以多种方式展开的影响。本文提出一个概念框架，以理解因果效应的时间动态及其对研究假设与设计的意义。随后，我们引入贝叶斯变点模型（BCMs）作为一种方法工具，用于检测因果效应序列的均值、方差或趋势的转变，为目视检查图形提供一种更严谨且易于使用的替代方案。接下来，我们通过一个基于模拟数据的示例以及一个考察随身摄像头对警察使用武力影响的真实案例，展示BCMs的应用。最后，我们讨论考察效应的时间变化如何促进对效应为何以及如何发生的理论理解，并为实践中政策与战略的设计和实施提供参考。
关键词
公共行政、政策研究、因果效应、时序演变、贝叶斯变点模型、准实验、随身摄像头、警察使用武力