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Regression modelQuasi-experimental / causal inference

Machine Learning-Augmented Doubly Robust Estimation (ML-DR)

Machine learning-augmented doubly robust (ML-DR) estimation combines the classical doubly robust (AIPW) identification strategy with flexible machine learning models for the nuisance functions — the propensity score and the outcome regression. The result is a causal estimator that is consistent if either ML component is correctly specified, and that achieves valid, root-n inference even when the nuisance models are estimated with high-dimensional regularisation or nonparametric learners.

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Sources

  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 ↗

Related methods

Difference-in-DifferencesDoubly Robust EstimationInverse Probability WeightingMachine Learning-Augmented Propensity Score MatchingMarginal Structural ModelPropensity Score Weighting

Referenced by

Heterogeneous treatment effect Doubly robust estimationMachine Learning-Augmented Inverse Probability WeightingMachine Learning-Augmented Marginal Structural ModelMachine Learning-Augmented Matching EstimatorMachine Learning-Augmented Propensity Score Matching

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ScholarGate — Machine learning-augmented doubly robust estimation (Machine Learning-Augmented Doubly Robust Estimation). Retrieved 2026-06-04 from https://scholargate.app/tr/causal-inference/machine-learning-augmented-doubly-robust-estimation