Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Heterogena behandlingseffekter (CATE / Meta-inlärningsmetoder)× | Algoritmer för kausal upptäckt (PC, FCI, LiNGAM)× | |
|---|---|---|
| Ämnesområde | Kausal inferens | Kausal inferens |
| Familj | Regression model | Regression model |
| Ursprungsår≠ | 2018 | 2000 |
| Upphovsperson≠ | Wager & Athey (causal forest); Künzel et al. (meta-learners) | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) |
| Typ≠ | Causal machine-learning framework | Causal structure learning |
| Ursprungskälla≠ | Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association. DOI ↗ | Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402 |
| Alias≠ | conditional average treatment effect, CATE, meta-learners, causal forest | PC algorithm, FCI algorithm, LiNGAM, causal structure learning |
| Närliggande | 5 | 5 |
| Sammanfattning≠ | 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). | Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits linear non-Gaussian structure to orient edges. |
| ScholarGateDatamängd ↗ |
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