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| Cân bằng điểm khuynh hướng bằng trọng số cho Đánh giá Chính sách× | Ghép cặp điểm xu hướng× | |
|---|---|---|
| Lĩnh vực≠ | Suy luận nhân quả | Thống kê nghiên cứu |
| Họ≠ | Regression model | Process / pipeline |
| Năm ra đời≠ | 1983/2003 | 1983 |
| Người khởi xướng≠ | Rosenbaum & Rubin (1983); extended to policy evaluation by Hirano, Imbens & Ridder (2003) | Paul Rosenbaum and Donald Rubin |
| Loại≠ | Quasi-experimental causal inference | Method |
| Công trình gốc≠ | Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189. 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 ↗ |
| Tên gọi khác≠ | PSW policy evaluation, inverse probability weighting for policy, IPW policy evaluation, policy PSW | PSM, propensity score weighting, covariate balance |
| Liên quan≠ | 6 | 3 |
| Tóm tắt≠ | Policy evaluation propensity score weighting applies inverse-probability weighting to observational data to estimate the causal effect of a policy program. By reweighting participants and non-participants so they resemble a target population, it removes selection bias from voluntary or administratively allocated program assignment without requiring randomization. | 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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