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| 다기간 이중 강건 추정× | 성향 점수 매칭× | |
|---|---|---|
| 분야≠ | 인과추론 | 연구 통계 |
| 계열≠ | Regression model | Process / pipeline |
| 기원 연도≠ | 1994-2021 | 1983 |
| 창시자≠ | Robins, Rotnitzky, and Zhao; extended by Bang & Robins (2005) and Callaway & Sant'Anna (2021) | Paul Rosenbaum and Donald Rubin |
| 유형≠ | Semiparametric causal estimator | Method |
| 원전≠ | Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973. 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≠ | longitudinal DR estimation, multi-period DR, multi-wave doubly robust, sequential doubly robust estimation | PSM, propensity score weighting, covariate balance |
| 관련≠ | 6 | 3 |
| 요약≠ | Multi-period doubly robust (DR) estimation extends the classic doubly robust approach to longitudinal settings with multiple treatment periods and time points. It combines an outcome regression model and a propensity score model for each period, retaining consistency of the causal effect estimate as long as at least one of the two models is correctly specified at every time point. | 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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