Salīdzināt metodes

	Panelu datu saskaņošanas novērtētājs	Novērtēšanas vienādošana (Matching Estimator)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	1997-2021	1973
Autors	Heckman, Ichimura & Todd (1997); Imai, Kim & Wang (2021) for panel extension	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Tips	Quasi-experimental causal estimator	Nonparametric matching / causal inference
Pirmavots	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.	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Citi nosaukumi	panel matching, matching-on-panel-data, longitudinal matching estimator, PDME	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Saistītās	6	6
Kopsavilkums	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.	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.