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| 동적 매칭 추정량× | 패널 데이터 매칭 추정량× | |
|---|---|---|
| 분야 | 인과추론 | 인과추론 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2010 | 1997-2021 |
| 창시자≠ | Lechner & Miquel (2010); building on Heckman, Ichimura & Todd (1998) | Heckman, Ichimura & Todd (1997); Imai, Kim & Wang (2021) for panel extension |
| 유형≠ | Nonparametric causal inference / matching | Quasi-experimental causal estimator |
| 원전≠ | Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137. DOI ↗ | 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 ↗ |
| 별칭 | dynamic treatment matching, sequential matching estimator, dynamic selection-on-observables, DME | panel matching, matching-on-panel-data, longitudinal matching estimator, PDME |
| 관련 | 6 | 6 |
| 요약≠ | 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 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. |
| ScholarGate데이터셋 ↗ |
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