Porovnat metody

	Odhadování s rozšířenou dvojitou robustností (ML-DR) pomocí strojového učení	Vážená inverzní pravděpodobnost léčby (IPW / IPTW)
Obor	Kauzální inference	Kauzální inference
Rodina	Regression model	Regression model
Rok vzniku	2018	2000
Tvůrce	Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey & Robins	Robins, Hernán & Brumback
Typ	Semiparametric causal estimator with ML nuisance	Causal inference weighting estimator
Původní zdroj	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.
Další názvy	ML-DR, AIPW with ML, Double/Debiased ML doubly robust, DML-DR	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Příbuzné	6	5
Shrnutí	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.