Порівняння методів

	Зважування за показником схильності (PSW / IPW)	Згруповане точне зіставлення (CEM)
Галузь	Причинно-наслідковий висновок	Причинно-наслідковий висновок
Родина	Regression model	Regression model
Рік появи	1983 (propensity score); 2003 (efficient IPW estimator)	2011-2012
Автор методу	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)	Iacus, King, & Porro
Тип	Causal inference / reweighting	Matching / causal inference
Основоположне джерело	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.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Інші назви	PSW, inverse probability weighting, IPW, propensity-based weighting	CEM, coarsened matching, monotonic imbalance bounding matching
Пов'язані	6	6
Підсумок	Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).	Coarsened Exact Matching is a preprocessing method that achieves covariate balance by temporarily coarsening continuous variables into bins, exactly matching treated and control units within those bins, and then discarding all unmatched units. Introduced by Iacus, King, and Porro (2011, 2012), it bounds imbalance on each covariate independently, yielding a matched sample on which any estimator can be applied without relying on a propensity score model.