Bandingkan metode

	Evaluasi Kebijakan Melalui Pencocokan Tepat yang Dikasarankan (CEM)	Bobot Probabilitas Invers (IPW / IPTW)
Bidang	Inferensi Kausal	Inferensi Kausal
Keluarga	Regression model	Regression model
Tahun asal	2011-2012	2000
Pencetus	Iacus, King & Porro	Robins, Hernán & Brumback
Tipe	Matching / quasi-experimental design	Causal inference weighting estimator
Sumber perintis	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.
Alias	CEM, Coarsened Exact Matching, CEM policy evaluation, coarsening-based matching	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Terkait	5	5
Ringkasan	Coarsened Exact Matching (CEM) is a quasi-experimental causal-inference technique that creates balanced treatment and control groups from observational data by temporarily coarsening covariates into bins, exactly matching units within those bins, and then pruning unmatched observations before estimating policy effects. Introduced by Iacus, King, and Porro, CEM belongs to the monotonic imbalance bounding family of matching methods and is especially popular in policy evaluation.	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.