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تصميم دراسة الحدث لتقييم السياسات×تصميم الانحدار المقطوع (RDD)×
المجالالاستدلال السببيالاستدلال السببي
العائلةRegression modelRegression model
سنة النشأة1993-20212008
صاحب الطريقةAndrews (1993), MacKinlay (1997); formalized for policy evaluation by Freyaldenhoven, Hansen & Shapiro (2019) and Callaway & Sant'Anna (2021)Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
النوعQuasi-experimental / causal inferenceQuasi-experimental causal design
المصدر التأسيسيCallaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗
الأسماء البديلةevent study, event-study DiD, dynamic DiD, PEESDRDD, regression discontinuity design, sharp RDD, fuzzy RDD
ذات صلة55
الملخصA policy evaluation event study design is a quasi-experimental approach that estimates causal effects of a policy by plotting treatment-period-by-period coefficients around a common event time. It extends difference-in-differences to visualize both pre-treatment parallel trends and the dynamic post-treatment evolution of the policy effect, and has become the standard credibility check in applied policy research.Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.
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ScholarGateقارن الطرق: Policy Evaluation Event Study Design · Regression Discontinuity. استُرجع بتاريخ 2026-06-18 من https://scholargate.app/ar/compare