विधियों की तुलना करें

	प्रोपेंसिटी स्कोर वेटिंग (PSW / IPW)	कोर्स्ड एग्जैक्ट मैचिंग (CEM)
क्षेत्र	कारणात्मक अनुमान	कारणात्मक अनुमान
परिवार	Regression model	Regression model
उद्भव वर्ष	1983 (propensity score); 2003 (efficient IPW estimator)	2011-2012
प्रवर्तक	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)	Iacus, King, & Porro
प्रकार	Causal inference / reweighting	Matching / causal inference
मौलिक स्रोत	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.
उपनाम	PSW, inverse probability weighting, IPW, propensity-based weighting	CEM, coarsened matching, monotonic imbalance bounding matching
संबंधित	6	6
सारांश	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.