Methoden vergelijken

	Coarsened Exact Matching (CEM)	Inverse Probability of Treatment Weighting (IPW / IPTW)
Vakgebied	Causale inferentie	Causale inferentie
Familie	Regression model	Regression model
Jaar van ontstaan	2011-2012	2000
Grondlegger	Iacus, King, & Porro	Robins, Hernán & Brumback
Type	Matching / causal inference	Causal inference weighting estimator
Oorspronkelijke bron	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Aliassen	CEM, coarsened matching, monotonic imbalance bounding matching	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Verwant	6	5
Samenvatting	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.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.