Porovnať metódy
Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| Lokálny priemerný efekt liečby (LATE / CACE)× | Heterogénne kauzálne efekty (CATE / Meta-Learners)× | |
|---|---|---|
| Odbor | Kauzálna inferencia | Kauzálna inferencia |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 1994 | 2018 |
| Tvorca≠ | Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996) | Wager & Athey (causal forest); Künzel et al. (meta-learners) |
| Typ≠ | Instrumental-variable causal estimand | Causal machine-learning framework |
| Pôvodný zdroj≠ | Imbens, G. W., & Angrist, J. D. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2), 467-475. DOI ↗ | Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association. DOI ↗ |
| Ďalšie názvy≠ | LATE, CACE, complier average causal effect, Yerel Ortalama Tedavi Etkisi (LATE / CACE) | conditional average treatment effect, CATE, meta-learners, causal forest |
| Príbuzné | 5 | 5 |
| Zhrnutie≠ | The Local Average Treatment Effect is an instrumental-variable estimand, introduced by Imbens and Angrist (1994) and formalised with Rubin (1996), that recovers the average treatment effect for the subpopulation of compliers — units whose treatment status is actually moved by the instrument. It is closely tied to compliance analysis. | 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). |
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