Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzperiodu saskaņošanas novērtētājs× | Novērtēšanas vienādošana (Matching Estimator)× | |
|---|---|---|
| Nozare | Cēloņsakarību secināšana | Cēloņsakarību secināšana |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2005 | 1973 |
| Autors≠ | Abadie (2005); Imbens & Wooldridge (2009) | Rubin (1973); large-sample theory by Abadie & Imbens (2006) |
| Tips≠ | Quasi-experimental / causal inference | Nonparametric matching / causal inference |
| Pirmavots≠ | Abadie, A. (2005). Semiparametric Difference-in-Differences Estimators. Review of Economic Studies, 72(1), 1-19. 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 | panel matching estimator, longitudinal matching, multi-wave matching, repeated-cross-section matching | nearest-neighbor matching, NNM, matching on covariates, covariate matching |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | The multi-period matching estimator extends the standard matching framework to settings with multiple time periods, pairing each treated unit to similar untreated units based on pre-treatment covariates or propensity scores, then using within-pair before-after differences to estimate the average treatment effect on the treated (ATT). Leveraging repeated observations, it simultaneously controls for observed confounders and time-invariant unobserved heterogeneity. | 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. |
| ScholarGateDatu kopa ↗ |
|
|