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Regression modelQuasi-experimental / causal inference

Maskinlærings-augmenteret dobbelt robust estimering (ML-DR)

Maskinlærings-augmenteret dobbelt robust (ML-DR) estimering kombinerer den klassiske dobbelt robuste (AIPW) identifikationsstrategi med fleksible maskinlæringsmodeller for forstyrrelsesfunktionerne – propensity scoren og outcome-regressionen. Resultatet er en kausal estimator, der er konsistent, hvis enten ML-komponenten er korrekt specificeret, og som opnår gyldig, rod-n inferens, selv når forstyrrelsesmodellerne estimeres med højdimensionel regularisering eller ikke-parametriske læringsalgoritmer.

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Kilder

  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. Farrell, M. H., Liang, T., & Misra, S. (2021). Deep Neural Networks for Estimation and Inference. Econometrica, 89(1), 181-213. DOI: 10.3982/ECTA16901

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ScholarGate. (2026, June 3). Machine Learning-Augmented Doubly Robust Estimation. ScholarGate. https://scholargate.app/da/causal-inference/machine-learning-augmented-doubly-robust-estimation

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ScholarGateMachine learning-augmented doubly robust estimation (Machine Learning-Augmented Doubly Robust Estimation). Hentet 2026-06-15 fra https://scholargate.app/da/causal-inference/machine-learning-augmented-doubly-robust-estimation · Datasæt: https://doi.org/10.5281/zenodo.20539026