방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 성향 점수 가중치 (PSW / IPW)× | 성향 점수 매칭× | |
|---|---|---|
| 분야≠ | 인과추론 | 연구 통계 |
| 계열≠ | Regression model | Process / pipeline |
| 기원 연도≠ | 1983 (propensity score); 2003 (efficient IPW estimator) | 1983 |
| 창시자≠ | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) | Paul Rosenbaum and Donald Rubin |
| 유형≠ | Causal inference / reweighting | Method |
| 원전≠ | 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 ↗ | 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 ↗ |
| 별칭≠ | PSW, inverse probability weighting, IPW, propensity-based weighting | PSM, propensity score weighting, covariate balance |
| 관련≠ | 6 | 3 |
| 요약≠ | Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003). | 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. |
| ScholarGate데이터셋 ↗ |
|
|