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| 정책 평가 역확률 가중치 (Policy Evaluation Inverse Probability Weighting)× | Marginal Structural Model (MSM)× | |
|---|---|---|
| 분야 | 인과추론 | 인과추론 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1952 (IPW origin); 2000s (policy evaluation application) | 2000 |
| 창시자≠ | Horvitz & Thompson (1952); extended to causal policy settings by Robins, Hernan & Brumback (2000) and Imbens & Wooldridge (2009) | James M. Robins, Miguel A. Hernan, Babette Brumback |
| 유형≠ | Reweighting estimator for causal policy analysis | Causal model / semiparametric weighting |
| 원전≠ | Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86. DOI ↗ | Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| 별칭≠ | IPW policy evaluation, propensity-weighted policy analysis, inverse probability of treatment weighting | MSM, MSM-IPTW, marginal structural Cox model, weighted structural model |
| 관련≠ | 6 | 5 |
| 요약≠ | Policy evaluation inverse probability weighting (IPW) uses estimated propensity scores to reweight observed units so that the weighted sample mimics a randomised experiment. Each unit is weighted by the inverse of its probability of receiving the policy, creating a pseudo-population in which treatment assignment is independent of observed covariates and the average treatment effect (ATE) can be read off directly. | A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population in which confounding is eliminated, enabling unbiased estimation of causal treatment contrasts even when standard regression adjustments would fail. |
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