Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Panel Data Coarsened Exact Matching (Panel Data CEM)× | Coarsened Exact Matching (CEM)× | |
|---|---|---|
| Nozare | Cēloņsakarību secināšana | Cēloņsakarību secināšana |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2012 (CEM); 2021 (panel extension) | 2011-2012 |
| Autors≠ | Iacus, King & Porro (CEM, 2012); panel extension via Imai, Kim & Wang (2021) | Iacus, King, & Porro |
| Tips≠ | Matching / quasi-experimental | 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 ↗ | Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. DOI ↗ |
| Citi nosaukumi | Panel CEM, CEM for panel data, coarsened exact matching with panel data | CEM, coarsened matching, monotonic imbalance bounding 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. | Coarsened Exact Matching is a preprocessing method that achieves covariate balance by temporarily coarsening continuous variables into bins, exactly matching treated and control units within those bins, and then discarding all unmatched units. Introduced by Iacus, King, and Porro (2011, 2012), it bounds imbalance on each covariate independently, yielding a matched sample on which any estimator can be applied without relying on a propensity score model. |
| ScholarGateDatu kopa ↗ |
|
|