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	बहु-अवधि फजी रिग्रेशन डिसकंटीन्यूइटी डिज़ाइन	फजी रिग्रेशन डिसकंटीन्यूइटी डिज़ाइन
क्षेत्र	कारणात्मक अनुमान	कारणात्मक अनुमान
परिवार	Regression model	Regression model
उद्भव वर्ष	2001 (fuzzy RD); multi-period extension ~2010s	2001
प्रवर्तक	Hahn, Todd & Van der Klaauw (foundational fuzzy RD, 2001); extended to multi-period settings by Cattaneo, Idrobo & Titiunik and subsequent applied literature	Hahn, Todd & van der Klaauw
प्रकार	Quasi-experimental causal inference	Quasi-experimental causal inference
मौलिक स्रोत	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.	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.
उपनाम	multi-period fuzzy RDD, fuzzy RD with repeated assignment, multi-wave fuzzy RD, staggered fuzzy RDD	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD
संबंधित	4	5
सारांश	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.	Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates causal effects when eligibility for a treatment is determined by a threshold on a running variable but actual take-up of that treatment is imperfect — some eligible units do not receive treatment and some ineligible units do. The cutoff acts as an instrument, and the estimand is a Local Average Treatment Effect (LATE) for compliers near the threshold.