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| Estimatore per la Valutazione delle Politiche basato sul Matching× | Disegno a Regressione Discontinua (RDD)× | |
|---|---|---|
| Campo | Inferenza causale | Inferenza causale |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1998-2006 | 2008 |
| Ideatore≠ | Heckman, Ichimura & Todd; Abadie & Imbens | Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction) |
| Tipo≠ | Non-parametric causal estimator | Quasi-experimental causal design |
| Fonte seminale≠ | Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235-267. DOI ↗ | Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗ |
| Alias≠ | matching estimator, program evaluation matching, treatment effect matching, Abadie-Imbens estimator | RDD, regression discontinuity design, sharp RDD, fuzzy RDD |
| Correlati≠ | 6 | 5 |
| Sintesi≠ | The policy evaluation matching estimator estimates the causal effect of a program or policy on treated units by pairing each participant with one or more non-participants who share similar pre-treatment characteristics. Developed rigorously by Heckman, Ichimura & Todd (1998) and Abadie & Imbens (2006), it avoids parametric outcome models and is the standard non-parametric tool for program and policy evaluation. | Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold. |
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