Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Metodes (CEM / Optimālā / Ģenētiskā)× | Heterogēni ārstēšanas efekti (CATE / Metamācītāji)× | |
|---|---|---|
| Nozare | Cēloņsakarību secināšana | Cēloņsakarību secināšana |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2012 | 2018 |
| Autors≠ | Iacus, King & Porro (CEM); Hansen (optimal/full matching) | Wager & Athey (causal forest); Künzel et al. (meta-learners) |
| Tips≠ | Matching for causal inference | Causal machine-learning framework |
| Pirmavots≠ | Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. DOI ↗ | Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association. DOI ↗ |
| Citi nosaukumi≠ | coarsened exact matching, optimal matching, genetic matching, CEM | conditional average treatment effect, CATE, meta-learners, causal forest |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Matching Methods are a family of causal-inference techniques beyond propensity-score matching that pair treated and control units with similar covariates so that a treatment effect can be read off the balanced sample. The family includes Coarsened Exact Matching (Iacus, King & Porro, 2012), optimal matching, and genetic matching. | 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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