Compara mètodes

	Machine Learning-Augmented Inverse Probability Weighting	Pes pesat per la probabilitat inversa (IPW / IPTW)
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	2003-2018	2000
Autor original	Hirano, Imbens & Ridder (semiparametric foundation, 2003); Chernozhukov et al. (DML framework, 2018)	Robins, Hernán & Brumback
Tipus	Semiparametric causal estimator	Causal inference weighting estimator
Font seminal	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.
Àlies	ML-IPW, nonparametric IPW, data-adaptive IPW, ML-augmented propensity weighting	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Relacionats	5	5
Resum	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.