Regression modelQuasi-experimental / causal inference

Machine Learning-Augmented Coarsened Exact Matching (ML-CEM)

Machine Learning-Augmented Coarsened Exact Matching extends Coarsened Exact Matching (Iacus, King & Porro, 2012) by using supervised machine learning to automate and optimise the coarsening step — the discretisation of continuous covariates into bins — rather than relying on researcher-specified cutpoints. This reduces both ad hoc subjectivity in coarsening decisions and residual imbalance, while preserving CEM's core logic of exact matching within coarsened strata.

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Sources

  1. Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. DOI: 10.1093/pan/mpr013
  2. Imai, K., & Ratkovic, M. (2014). Covariate balancing propensity score. Journal of the Royal Statistical Society: Series B, 76(1), 243-263. DOI: 10.1111/rssb.12027

Related methods

ScholarGateMachine Learning-Augmented Coarsened Exact Matching (Machine Learning-Augmented Coarsened Exact Matching Estimator). Retrieved 2026-06-04 from https://scholargate.app/en/causal-inference/machine-learning-augmented-coarsened-exact-matching