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| Dynamischer Matching-Schätzer× | Matching-Schätzer× | |
|---|---|---|
| Fachgebiet | Kausale Inferenz | Kausale Inferenz |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 2010 | 1973 |
| Urheber≠ | Lechner & Miquel (2010); building on Heckman, Ichimura & Todd (1998) | Rubin (1973); large-sample theory by Abadie & Imbens (2006) |
| Typ≠ | Nonparametric causal inference / matching | Nonparametric matching / causal inference |
| Wegweisende Quelle≠ | Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137. DOI ↗ | Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267. DOI ↗ |
| Aliasnamen | dynamic treatment matching, sequential matching estimator, dynamic selection-on-observables, DME | nearest-neighbor matching, NNM, matching on covariates, covariate matching |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | The Dynamic Matching Estimator extends standard matching methods to settings where treatment is assigned sequentially over multiple periods. Instead of a single treatment decision, units receive or forgo treatment at each time point, and the estimator identifies causal effects of entire treatment histories by matching on time-varying covariates and past treatment paths, under sequential conditional independence assumptions. | 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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