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| 동적 엔트로피 균형 (Dynamic Entropy Balancing)× | 역확률 가중치 (Inverse Probability Weighting, IPW / IPTW)× | |
|---|---|---|
| 분야 | 인과추론 | 인과추론 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2012-2018 | 2000 |
| 창시자≠ | Hainmueller (2012) for static entropy balancing; extended to dynamic settings by Blackwell and Glynn (2018) and subsequent methodologists | Robins, Hernán & Brumback |
| 유형≠ | Causal inference / weighting estimator | Causal inference weighting estimator |
| 원전≠ | Hainmueller, J. (2012). Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies. Political Analysis, 20(1), 25-46. DOI ↗ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| 별칭≠ | DEB, longitudinal entropy balancing, entropy balancing with time-varying treatment, sequential entropy balancing | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| 관련≠ | 6 | 5 |
| 요약≠ | Dynamic Entropy Balancing extends the entropy balancing reweighting approach to settings with time-varying treatments in panel or longitudinal data. It constructs unit weights at each time period such that the covariate distributions of treated and comparison units are balanced on specified moments, adjusting sequentially for prior treatment history and time-varying confounders to estimate the causal effect of treatment sequences on outcomes. | Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias. |
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