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	Maskininlärningsförstärkt invers sannolikhetsviktning (ML-IPW)	Viktning med inversa sannolikheter för behandling (IPW / IPTW)
Ämnesområde	Kausal inferens	Kausal inferens
Familj	Regression model	Regression model
Ursprungsår	2003-2018	2000
Upphovsperson	Hirano, Imbens & Ridder (semiparametric foundation, 2003); Chernozhukov et al. (DML framework, 2018)	Robins, Hernán & Brumback
Typ	Semiparametric causal estimator	Causal inference weighting estimator
Ursprungskälla	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.
Alias	ML-IPW, nonparametric IPW, data-adaptive IPW, ML-augmented propensity weighting	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Närliggande	5	5
Sammanfattning	Machine learning-augmented inverse probability weighting replaces parametric logistic regression with flexible ML algorithms to estimate treatment propensity scores, then reweights the sample to balance treated and control units. By leveraging data-adaptive learners such as lasso, random forests, or gradient boosting, ML-IPW controls for high-dimensional and nonlinear confounders that classical IPW misses, while retaining the intuitive weighting framework.	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.