Methoden vergleichen
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| Paneldaten-Matching-Schätzer× | Matching-Schätzer× | |
|---|---|---|
| Fachgebiet | Kausale Inferenz | Kausale Inferenz |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1997-2021 | 1973 |
| Urheber≠ | Heckman, Ichimura & Todd (1997); Imai, Kim & Wang (2021) for panel extension | Rubin (1973); large-sample theory by Abadie & Imbens (2006) |
| Typ≠ | Quasi-experimental causal estimator | Nonparametric matching / causal inference |
| Wegweisende Quelle≠ | Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. Review of Economic Studies, 64(4), 605-654. DOI ↗ | Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267. DOI ↗ |
| Aliasnamen | panel matching, matching-on-panel-data, longitudinal matching estimator, PDME | nearest-neighbor matching, NNM, matching on covariates, covariate matching |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | The panel data matching estimator identifies causal treatment effects by pairing each treated unit with one or more control units that share similar covariate histories in the pre-treatment periods. By exploiting the longitudinal structure of panel data, it controls for both observed time-varying confounders and stable unit characteristics, estimating the average treatment effect on the treated (ATT) without requiring a parallel-trends assumption. | 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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