Comparer des méthodes

	Pondération par score de propension augmentée par apprentissage automatique	Pondération par l'inverse de la probabilité de traitement (IPW / IPTW)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2010–2018	2000
Auteur d'origine	Lee, Lessler & Stuart (2010); Chernozhukov et al. (2018, DML framework)	Robins, Hernán & Brumback
Type	Causal inference / semiparametric weighting	Causal inference weighting estimator
Source fondatrice	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-PSW, ML-augmented IPW, machine learning propensity weighting, nonparametric propensity score weighting	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Apparentées	5	5
Résumé	Machine learning-augmented propensity score weighting (ML-PSW) replaces logistic regression with flexible ML algorithms — such as gradient boosting, LASSO, or random forests — to estimate the propensity score, then uses inverse probability weights to balance treated and control groups. This reduces model-misspecification bias when the true relationship between covariates and treatment assignment is complex or high-dimensional.	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.