Linganisha mbinu

	Kikokotozi cha Kulinganisha kilichoimarishwa na Mashine ya Kujifunza	Uzito wa Kinyume wa Uwezekano wa Matibabu (IPW / IPTW)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2006–2018	2000
Mwanzilishi	Abadie & Imbens (classical matching); Chernozhukov et al. (ML augmentation framework)	Robins, Hernán & Brumback
Aina	Causal inference / nonparametric matching	Causal inference weighting estimator
Chanzo asilia	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.
Majina mbadala	ML-augmented matching, ML matching estimator, high-dimensional matching estimator, data-adaptive matching estimator	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Zinazohusiana	5	5
Muhtasari	The machine learning-augmented matching estimator combines classical nearest-neighbor or propensity-score matching with ML algorithms — such as lasso, random forests, or gradient boosting — to select covariates, estimate propensity scores, and correct for residual bias. The result is a matching-based causal estimator that remains valid under high-dimensional confounding where traditional hand-specified matching fails.	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.