Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Panel Data Coarsened Exact Matching (Panel Data CEM)× | Novērtēšanas vienādošana (Matching Estimator)× | |
|---|---|---|
| Nozare | Cēloņsakarību secināšana | Cēloņsakarību secināšana |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2012 (CEM); 2021 (panel extension) | 1973 |
| Autors≠ | Iacus, King & Porro (CEM, 2012); panel extension via Imai, Kim & Wang (2021) | Rubin (1973); large-sample theory by Abadie & Imbens (2006) |
| Tips≠ | Matching / quasi-experimental | Nonparametric matching / causal inference |
| Pirmavots≠ | Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. 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 CEM, CEM for panel data, coarsened exact matching with panel data | nearest-neighbor matching, NNM, matching on covariates, covariate matching |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | Panel Data Coarsened Exact Matching applies the Coarsened Exact Matching (CEM) algorithm to repeated-measures panel data, matching treated and control units within the same coarsened covariate strata across multiple time periods. It balances pre-treatment characteristics before estimating a causal treatment effect, combining the transparency of exact matching with the richer identification available in longitudinal datasets. | 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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