Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Heterogenní léčebné účinky (CATE / Meta-Learners)× | Instrumentální proměnné pomocí dvoufázové metody nejmenších čtverců (IV/2SLS)× | |
|---|---|---|
| Obor | Kauzální inference | Kauzální inference |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 2018 | 2009 |
| Tvůrce≠ | Wager & Athey (causal forest); Künzel et al. (meta-learners) | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) |
| Typ≠ | Causal machine-learning framework | Instrumental-variables regression |
| Původní zdroj≠ | 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 |
| Další názvy≠ | conditional average treatment effect, CATE, meta-learners, causal forest | instrumental variables, IV estimation, 2SLS, instrumental variable regression |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | 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). |
| ScholarGateDatová sada ↗ |
|
|