Salīdzināt metodes

	Mašīnmācīšanās papildinātā noslieces rādītāja svēršana	Divkārši robusta novērtēšana (AIPW)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2010–2018	2005
Autors	Lee, Lessler & Stuart (2010); Chernozhukov et al. (2018, DML framework)	Robins & Rotnitzky; Bang & Robins
Tips	Causal inference / semiparametric weighting	Semiparametric causal estimator
Pirmavots	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. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129.
Citi nosaukumi	ML-PSW, ML-augmented IPW, machine learning propensity weighting, nonparametric propensity score weighting	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Saistītās	5	5
Kopsavilkums	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.	Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified.