مقایسهٔ روش‌ها

	طراحی ناپیوستگی رگرسیون فازی چنددوره‌ای	رگرسیون ناپیوستگی داده‌های پانل
حوزه	استنتاج علّی	استنتاج علّی
خانواده	Regression model	Regression model
سال پیدایش	2001 (fuzzy RD); multi-period extension ~2010s	1960 (original RDD); panel extension codified 2000s–2010s
پدیدآور	Hahn, Todd & Van der Klaauw (foundational fuzzy RD, 2001); extended to multi-period settings by Cattaneo, Idrobo & Titiunik and subsequent applied literature	Thistlethwaite & Campbell (1960); panel extension developed through Lee & Lemieux (2010) and related applied work
نوع	Quasi-experimental causal inference	Causal inference / quasi-experimental
منبع بنیادین	Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209.	Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.
نام‌های دیگر	multi-period fuzzy RDD, fuzzy RD with repeated assignment, multi-wave fuzzy RD, staggered fuzzy RDD	Panel RD, Panel RDD, Longitudinal Regression Discontinuity, Fixed-Effects RDD
مرتبط	4	5
خلاصه	Multi-period fuzzy regression discontinuity design estimates a local average treatment effect when a cutoff rule only partially determines treatment — that is, crossing the threshold raises the probability of treatment but does not guarantee it — and when this assignment process is observed across two or more time periods or cohorts, enabling pooled or period-specific causal estimates under repeated near-threshold comparisons.	Panel data regression discontinuity design (Panel RDD) combines the sharp local identification of a regression discontinuity with the within-unit variation available in repeated-observation panel data. Units are observed across multiple periods, and treatment is assigned based on whether a running variable crosses a known threshold. By leveraging both the discontinuity and panel structure, researchers can control for unobserved unit-level heterogeneity while estimating a causal treatment effect near the threshold.