Võrdle meetodeid

	Masinõppimisega täiendatud sobitusestimaator	Sobivuse hindaja
Valdkond	Põhjuslik järeldamine	Põhjuslik järeldamine
Perekond	Regression model	Regression model
Tekkeaasta	2006–2018	1973
Looja	Abadie & Imbens (classical matching); Chernozhukov et al. (ML augmentation framework)	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Tüüp	Causal inference / nonparametric matching	Nonparametric matching / causal inference
Algallikas	Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Rööpnimetused	ML-augmented matching, ML matching estimator, high-dimensional matching estimator, data-adaptive matching estimator	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Seotud	5	6
Kokkuvõte	The machine learning-augmented matching estimator combines classical nearest-neighbor or propensity-score matching with ML algorithms — such as lasso, random forests, or gradient boosting — to select covariates, estimate propensity scores, and correct for residual bias. The result is a matching-based causal estimator that remains valid under high-dimensional confounding where traditional hand-specified matching fails.	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.