Sammenlign metoder

	Machine Learning-Augmented Matching Estimator	Dobbelt Robust Estimation (AIPW)
Fagområde	Kausal inferens	Kausal inferens
Familie	Regression model	Regression model
Oprindelsesår	2006–2018	2005
Ophavsperson	Abadie & Imbens (classical matching); Chernozhukov et al. (ML augmentation framework)	Robins & Rotnitzky; Bang & Robins
Type	Causal inference / nonparametric matching	Semiparametric causal estimator
Oprindelig kilde	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.	Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129.
Aliasser	ML-augmented matching, ML matching estimator, high-dimensional matching estimator, data-adaptive matching estimator	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Relaterede	5	5
Resumé	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.	Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified.