Salīdzināt metodes

	Aproksimēta novērtēšana (PSW / IPW)	Coarsened Exact Matching (CEM)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	1983 (propensity score); 2003 (efficient IPW estimator)	2011-2012
Autors	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)	Iacus, King, & Porro
Tips	Causal inference / reweighting	Matching / causal inference
Pirmavots	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.
Citi nosaukumi	PSW, inverse probability weighting, IPW, propensity-based weighting	CEM, coarsened matching, monotonic imbalance bounding matching
Saistītās	6	6
Kopsavilkums	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.