ScholarGate
Asistenti

Krahasoni metodat

Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.

Machine Learning-Augmented Inverse Probability Weighting×Pesha e Vlerësimit të Propensitetit (PSW / IPW)×
FushaInferenca kauzaleInferenca kauzale
FamiljaRegression modelRegression model
Viti i origjinës2003-20181983 (propensity score); 2003 (efficient IPW estimator)
KrijuesiHirano, Imbens & Ridder (semiparametric foundation, 2003); Chernozhukov et al. (DML framework, 2018)Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
LlojiSemiparametric causal estimatorCausal inference / reweighting
Burimi themeluesChernozhukov, 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. DOI ↗Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55. DOI ↗
Emërtime të tjeraML-IPW, nonparametric IPW, data-adaptive IPW, ML-augmented propensity weightingPSW, inverse probability weighting, IPW, propensity-based weighting
Të lidhura56
PërmbledhjaMachine 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.Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).
ScholarGateSeti i të dhënave
  1. v1
  2. 2 Burimet
  3. PUBLISHED
  1. v1
  2. 2 Burimet
  3. PUBLISHED

Shko te kërkimi Shkarko diapozitivat

ScholarGateKrahasoni metodat: Machine Learning-Augmented Inverse Probability Weighting · Propensity Score Weighting. Marrë më 2026-06-18 nga https://scholargate.app/sq/compare