Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Хетерогенни ефекти от лечение (CATE / Мета-обучаващи алгоритми)× | Инструментални променливи чрез двуетапни най-малки квадрати (IV/2SLS)× | |
|---|---|---|
| Област | Причинно-следствено заключение | Причинно-следствено заключение |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2018 | 2009 |
| Създател≠ | Wager & Athey (causal forest); Künzel et al. (meta-learners) | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) |
| Тип≠ | Causal machine-learning framework | Instrumental-variables regression |
| Основополагащ източник≠ | Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association. DOI ↗ | Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| Други названия≠ | conditional average treatment effect, CATE, meta-learners, causal forest | instrumental variables, IV estimation, 2SLS, instrumental variable regression |
| Свързани | 5 | 5 |
| Резюме≠ | Heterogeneous Treatment Effects is a machine-learning framework that estimates how a treatment effect varies across individuals — the conditional average treatment effect (CATE). It bundles meta-learner strategies such as the T-Learner, S-Learner, X-Learner and R-Learner alongside the causal forest of Wager and Athey (2018) and Künzel et al. (2019). | IV/2SLS is a two-stage estimation method that recovers the causal effect of an endogenous regressor by isolating the part of its variation driven by an external instrument. It is the workhorse identification strategy in modern applied econometrics, developed at length in Angrist and Pischke's Mostly Harmless Econometrics (2009). |
| ScholarGateНабор от данни ↗ |
|
|