방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 다기간 성향 점수 가중치× | 성향 점수 가중치 (PSW / IPW)× | |
|---|---|---|
| 분야 | 인과추론 | 인과추론 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2000 | 1983 (propensity score); 2003 (efficient IPW estimator) |
| 창시자≠ | Robins, Hernán, and Brumback (building on Robins' g-computation framework) | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| 유형≠ | Quasi-experimental causal inference | Causal inference / reweighting |
| 원전≠ | Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC. link ↗ | 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 | longitudinal propensity score weighting, multi-wave PSW, time-varying propensity score weighting, sequential propensity score weighting | PSW, inverse probability weighting, IPW, propensity-based weighting |
| 관련≠ | 5 | 6 |
| 요약≠ | Multi-period propensity score weighting extends the standard propensity score weighting framework to settings with repeated measurements and time-varying treatments. It constructs stabilised inverse probability weights (IPW) at each time point so that the weighted sample resembles a sequence of randomised experiments, allowing unbiased estimation of causal effects under longitudinal confounding. | 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). |
| ScholarGate데이터셋 ↗ |
|
|