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رگرسیون فازی ناپیوستگی (Fuzzy Regression Discontinuity Design)×تطابق امتیاز تمایل (Propensity Score Matching)×
حوزهاستنتاج علّیآمار پژوهش
خانوادهRegression modelProcess / pipeline
سال پیدایش20011983
پدیدآورHahn, Todd & van der KlaauwPaul Rosenbaum and Donald Rubin
نوعQuasi-experimental causal inferenceMethod
منبع بنیادینHahn, 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. DOI ↗Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. DOI ↗
نام‌های دیگرFuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDDPSM, propensity score weighting, covariate balance
مرتبط53
خلاصهFuzzy 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.Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.
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ScholarGateمقایسهٔ روش‌ها: Fuzzy Regression Discontinuity · Propensity Score Matching. بازیابی‌شده در 2026-06-18 از https://scholargate.app/fa/compare