방법 비교

	베이지안 사건 연구 설계	시계열 단절 분석 (Interrupted Time Series, ITS)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	1990s–2010s	2002
창시자	Developed from classical event study methodology (Fama et al., 1969) with Bayesian extensions proposed through the 1990s–2010s	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
유형	Quasi-experimental / causal inference	Quasi-experimental segmented regression
원전	Sorescu, A., Warren, N. L., & Ertekin, L. (2017). Event study methodology in the marketing literature: An overview. Journal of the Academy of Marketing Science, 45(2), 186-207.	Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.
별칭	Bayesian event study, Bayesian abnormal return estimation, Bayesian pre-post event analysis, BES	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
관련	5	5
요약	Bayesian Event Study Design extends the classical event study framework by replacing frequentist significance testing with a full Bayesian inferential framework. It estimates how an event (policy change, announcement, shock) alters an outcome trajectory by learning a prior model from the estimation window and updating it with observed data, yielding posterior distributions over abnormal effects and cumulative causal impacts with full uncertainty quantification.	Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope.