Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Regresijas atšķirības dizains (RDD)× | Propensity Score Matching× | |
|---|---|---|
| Nozare≠ | Ekonometrija | Pētniecības statistika |
| Saime≠ | Regression model | Process / pipeline |
| Izcelsmes gads≠ | 2008 | 1983 |
| Autors≠ | Imbens & Lemieux; Lee & Lemieux (modern practice); Cattaneo, Idrobo & Titiunik | Paul Rosenbaum and Donald Rubin |
| Tips≠ | Quasi-experimental causal design | Method |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi≠ | RDD, regression discontinuity, sharp regression discontinuity, Regresyon Süreksizliği Tasarımı (RDD) | PSM, propensity score weighting, covariate balance |
| Saistītās≠ | 5 | 3 |
| Kopsavilkums≠ | 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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