Mapping the Temporal Evolution of Causal Effects in Public Administration and Policy Research
本文提出一个概念框架来理解因果效应随时间变化的动态，并引入贝叶斯变点模型作为检测效应变化的方法，通过模拟数据和真实案例（随身摄像头对警察使用武力的影响）展示其应用。
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.