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Regression modelQuasi-experimental / causal inference

Uzito wa Uwezekano wa Kinyume Ulioboreshwa kwa Kujifunza kwa Mashine (ML-IPW)

Uzito wa uwezekano wa kinyume ulioboreshwa kwa kujifunza kwa mashine hubadilisha urejeshaji wa kimahesabu wa logistic na algoriti zinazonyumbulika za ML ili kukadiria alama za mwelekeo wa matibabu, kisha hupima upya sampuli ili kusawazisha vitengo vilivyotibiwa na vya udhibiti. Kwa kutumia wanafunzi wanaojirekebisha kulingana na data kama vile lasso, misitu ya nasibu, au nyongeza ya gradient, ML-IPW hudhibiti vigezo-changanyishi vya hali ya juu na visivyo vya mstari ambavyo IPW ya kawaida hukosa, huku ikihifadhi mfumo wa upimaji uzito unaoeleweka.

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Vyanzo

  1. 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. DOI: 10.1111/ectj.12097
  2. Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161-1189. DOI: 10.1111/1468-0262.00442

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Machine Learning-Augmented Inverse Probability Weighting Estimator. ScholarGate. https://scholargate.app/sw/causal-inference/machine-learning-augmented-inverse-probability-weighting

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ScholarGateMachine Learning-Augmented Inverse Probability Weighting (Machine Learning-Augmented Inverse Probability Weighting Estimator). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/causal-inference/machine-learning-augmented-inverse-probability-weighting · Seti ya data: https://doi.org/10.5281/zenodo.20539026