Mapping the temporal evolution of causal effects in public administration and policy research
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JPART — 2026-06-15
Original Mapping the temporal evolution of causal effects in public administration and policy research
Lefteris Jason Anastasopoulos, Inkyu Kang
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.