Vertaile menetelmiä

	Koneoppimista hyödyntävä kaksinkertaisesti robusti estimointi (ML-DR)	Käänteisen todennäköisyyden painotus (IPW / IPTW)
Tieteenala	Kausaalipäättely	Kausaalipäättely
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	2018	2000
Kehittäjä	Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey & Robins	Robins, Hernán & Brumback
Tyyppi	Semiparametric causal estimator with ML nuisance	Causal inference weighting estimator
Alkuperäislähde	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., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Rinnakkaisnimet	ML-DR, AIPW with ML, Double/Debiased ML doubly robust, DML-DR	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Liittyvät	6	5
Tiivistelmä	Machine learning-augmented doubly robust (ML-DR) estimation combines the classical doubly robust (AIPW) identification strategy with flexible machine learning models for the nuisance functions — the propensity score and the outcome regression. The result is a causal estimator that is consistent if either ML component is correctly specified, and that achieves valid, root-n inference even when the nuisance models are estimated with high-dimensional regularisation or nonparametric learners.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.