Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Динамічний оцінювач відповідності× | Зважування за оберненою ймовірністю лікування (IPW / IPTW)× | |
|---|---|---|
| Галузь | Причинно-наслідковий висновок | Причинно-наслідковий висновок |
| Родина | Regression model | Regression model |
| Рік появи≠ | 2010 | 2000 |
| Автор методу≠ | Lechner & Miquel (2010); building on Heckman, Ichimura & Todd (1998) | Robins, Hernán & Brumback |
| Тип≠ | Nonparametric causal inference / matching | Causal inference weighting estimator |
| Основоположне джерело≠ | Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137. DOI ↗ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Інші назви≠ | dynamic treatment matching, sequential matching estimator, dynamic selection-on-observables, DME | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | The Dynamic Matching Estimator extends standard matching methods to settings where treatment is assigned sequentially over multiple periods. Instead of a single treatment decision, units receive or forgo treatment at each time point, and the estimator identifies causal effects of entire treatment histories by matching on time-varying covariates and past treatment paths, under sequential conditional independence assumptions. | 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. |
| ScholarGateНабір даних ↗ |
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