विधियों की तुलना करें

	Coarsened Exact Matching (CEM) द्वारा नीति मूल्यांकन	उपचार भारण की व्युत्क्रम प्रायिकता (IPW / IPTW)
क्षेत्र	कारणात्मक अनुमान	कारणात्मक अनुमान
परिवार	Regression model	Regression model
उद्भव वर्ष	2011-2012	2000
प्रवर्तक	Iacus, King & Porro	Robins, Hernán & Brumback
प्रकार	Matching / quasi-experimental design	Causal inference weighting estimator
मौलिक स्रोत	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.
उपनाम	CEM, Coarsened Exact Matching, CEM policy evaluation, coarsening-based matching	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
संबंधित	5	5
सारांश	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.