Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Heterogēno ārstēšanas efektu noslieces rādītāja saskaņošana× | Novērtēšanas vienādošana (Matching Estimator)× | |
|---|---|---|
| Nozare | Cēloņsakarību secināšana | Cēloņsakarību secināšana |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1983–2016 | 1973 |
| Autors≠ | Rosenbaum & Rubin (PSM foundation, 1983); Athey & Imbens (HTE extensions, 2016) | Rubin (1973); large-sample theory by Abadie & Imbens (2006) |
| Tips≠ | Causal inference / matching with effect heterogeneity | Nonparametric matching / causal inference |
| Pirmavots≠ | Athey, S., & Imbens, G. W. (2016). Recursive Partitioning for Heterogeneous Causal Effects. Proceedings of the National Academy of Sciences, 113(27), 7353-7360. DOI ↗ | Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267. DOI ↗ |
| Citi nosaukumi | HTE-PSM, CATE via PSM, subgroup treatment effect matching, conditional average treatment effect matching | nearest-neighbor matching, NNM, matching on covariates, covariate matching |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | Heterogeneous Treatment Effect Propensity Score Matching extends standard PSM to estimate how treatment effects vary across subgroups or individual characteristics. Rather than reporting a single average treatment effect, it uses the matched sample to estimate conditional average treatment effects (CATE), revealing which types of units benefit most or least from a treatment. | The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome. |
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