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	Machine Learning-Augmented Coarsened Exact Matching	Dopasowanie wyników skłonności
Dziedzina	Wnioskowanie przyczynowe	Statystyka w badaniach
Rodzina	Regression model	Process / pipeline
Rok powstania	2012-2019	1983
Twórca	Extension of Iacus, King & Porro (2012) CEM; ML integration developed in subsequent causal ML literature	Paul Rosenbaum and Donald Rubin
Typ	Matching / quasi-experimental	Method
Źródło pierwotne	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Inne nazwy	ML-augmented CEM, ML-CEM, automated coarsened exact matching, ML-assisted CEM	PSM, propensity score weighting, covariate balance
Pokrewne	6	3
Podsumowanie	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.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.