Methoden vergleichen

	Maschinelles Lernen-erweitertes Coarsened Exact Matching (ML-CEM)	Entropie-Balancierung
Fachgebiet	Kausale Inferenz	Kausale Inferenz
Familie	Regression model	Regression model
Entstehungsjahr	2012-2019	2012
Urheber	Extension of Iacus, King & Porro (2012) CEM; ML integration developed in subsequent causal ML literature	Jens Hainmueller
Typ	Matching / quasi-experimental	Covariate-balancing reweighting
Wegweisende Quelle	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46.
Aliasnamen	ML-augmented CEM, ML-CEM, automated coarsened exact matching, ML-assisted CEM	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Verwandt	6	6
Zusammenfassung	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.	Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensity-score trimming with a constrained maximum-entropy optimisation that achieves balance in a single step.