विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| रिग्रेशन डिसकंटीन्यूइटी डिज़ाइन (RDD)× | प्रोपेंसिटी स्कोर मैचिंग× | |
|---|---|---|
| क्षेत्र≠ | अर्थमिति | अनुसंधान सांख्यिकी |
| परिवार≠ | Regression model | Process / pipeline |
| उद्भव वर्ष≠ | 2008 | 1983 |
| प्रवर्तक≠ | Imbens & Lemieux; Lee & Lemieux (modern practice); Cattaneo, Idrobo & Titiunik | Paul Rosenbaum and Donald Rubin |
| प्रकार≠ | Quasi-experimental causal design | Method |
| मौलिक स्रोत≠ | Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. 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 ↗ |
| उपनाम≠ | RDD, regression discontinuity, sharp regression discontinuity, Regresyon Süreksizliği Tasarımı (RDD) | PSM, propensity score weighting, covariate balance |
| संबंधित≠ | 5 | 3 |
| सारांश≠ | Regression Discontinuity Design is a quasi-experimental method that estimates a local causal effect around a threshold (cutoff) value, comparing units just below and just above the cutoff as if they were almost randomly assigned. It is the design developed for applied practice by Imbens and Lemieux (2008) and by Lee and Lemieux (2010). | 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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