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Machine learning-augmented propensity score weighting/Evidence
Method evidence record

Machine learning-augmented propensity score weighting

Machine learning-augmented propensity score weighting (ML-PSW) replaces logistic regression with flexible ML algorithms — such as gradient boosting, LASSO, or random forests — to estimate the propensity score, then uses inverse probability weights to balance treated and control groups. This reduces model-misspecification bias when the true relationship between covariates and treatment assignment is complex or high-dimensional.

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Machine Learning-Augmented Propensity Score Weighting
Taxonomic method record · regression-model / causal-inference
  • 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
  • Lee, B. K., Lessler, J., & Stuart, E. A. (2010). Improving propensity score weighting using machine learning. Statistics in Medicine, 29(3), 337-346. · DOI 10.1002/sim.3782
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Related methods

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Same method familyDifference-in-Differencesmachine-suggested · Relational suggestion, not evidence.Same method familyDoubly Robust Estimationmachine-suggested · Relational suggestion, not evidence.Same method familyInverse Probability Weightingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMachine Learning-Augmented Propensity Score Matchingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketPropensity Score Weightingmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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