Σύγκριση μεθόδων

	Ενισχυμένες με Μηχανική Μάθηση Ενδογενείς Μεταβλητές (ML-IV)	Αντιστοίχιση Βαθμολογίας Προδιάθεσης
Πεδίο	Αιτιακή Συμπερασματολογία	Ερευνητική Στατιστική
Οικογένεια	Regression model	Process / pipeline
Έτος προέλευσης	2012-2018	1983
Δημιουργός	Belloni, Chernozhukov & Hansen; Chernozhukov et al.	Paul Rosenbaum and Donald Rubin
Τύπος	Causal inference / semi-parametric estimation	Method
Θεμελιώδης πηγή	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.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Εναλλακτικές ονομασίες	ML-IV, MLIV, Double/Debiased ML with IV, DML-IV	PSM, propensity score weighting, covariate balance
Συναφείς	4	3
Σύνοψη	Machine learning-augmented instrumental variables combines the causal identification power of classical IV with modern high-dimensional machine learning — using methods such as LASSO, random forests, or neural networks to select valid instruments and model nuisance functions, thereby improving first-stage fit and enabling valid inference even when the number of potential instruments or controls is large relative to the sample size.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.