Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Panelu datu tendenču rādītāju saskaņošana× | Novērtēšanas vienādošana (Matching Estimator)× | |
|---|---|---|
| Nozare | Cēloņsakarību secināšana | Cēloņsakarību secināšana |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1997-1998 | 1973 |
| Autors≠ | Heckman, Ichimura & Todd | Rubin (1973); large-sample theory by Abadie & Imbens (2006) |
| Tips≠ | Matching / causal inference | Nonparametric matching / causal inference |
| Pirmavots≠ | Heckman, J. J., Ichimura, H., & Todd, P. (1998). Matching as an Econometric Evaluation Estimator. Review of Economic Studies, 65(2), 261-294. 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 | PSM with panel data, longitudinal PSM, panel PSM, difference-in-differences PSM | nearest-neighbor matching, NNM, matching on covariates, covariate matching |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | Panel data propensity score matching combines the bias-reduction of PSM with the longitudinal structure of panel data, enabling causal estimation of treatment effects by matching treated and control units on observable pre-treatment characteristics and then differencing within matched pairs over time. Developed in the framework of Heckman, Ichimura, and Todd (1998), it is especially valuable when randomisation is infeasible and both selection on observables and time-varying confounding must be addressed simultaneously. | 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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