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| 贝叶斯观测性定量研究× | 倾向得分匹配× | |
|---|---|---|
| 领域≠ | 研究设计 | 研究统计学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1990s–2000s (systematic application to observational research) | 1983 |
| 提出者≠ | Thomas Bayes (foundational theorem, 1763); modern applied form developed by Sander Greenland, Andrew Gelman, and colleagues (1990s–2000s) | Paul Rosenbaum and Donald Rubin |
| 类型≠ | Quantitative non-experimental research design with Bayesian inference | Method |
| 开创性文献≠ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 | 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 ↗ |
| 别名≠ | Bayesian observational study, Bayesian non-experimental quantitative design, Bayesian causal observational analysis, BOQR | PSM, propensity score weighting, covariate balance |
| 相关≠ | 4 | 3 |
| 摘要≠ | Bayesian observational quantitative research applies Bayesian statistical inference to data collected without experimental manipulation — surveys, administrative records, registries, or secondary datasets. Instead of relying solely on p-values and confidence intervals, the analyst encodes prior knowledge about parameters as probability distributions, updates them with observed data via Bayes' theorem, and reports conclusions as posterior probability statements. The approach is especially valued in epidemiology, social science, and health services research where randomisation is impossible or unethical. | 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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