방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 패널 데이터 매칭 추정량× | Coarsened Exact Matching (CEM)× | |
|---|---|---|
| 분야 | 인과추론 | 인과추론 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1997-2021 | 2011-2012 |
| 창시자≠ | Heckman, Ichimura & Todd (1997); Imai, Kim & Wang (2021) for panel extension | Iacus, King, & Porro |
| 유형≠ | Quasi-experimental causal estimator | Matching / causal inference |
| 원전≠ | 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 ↗ | Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. DOI ↗ |
| 별칭≠ | panel matching, matching-on-panel-data, longitudinal matching estimator, PDME | CEM, coarsened matching, monotonic imbalance bounding matching |
| 관련 | 6 | 6 |
| 요약≠ | 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. | 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. |
| ScholarGate데이터셋 ↗ |
|
|