Sammenlign metoder

	Heterogen Behandlingseffekt Grovkornet Præcis Matching	Propensity Score Matching
Fagområde	Kausal inferens	Forskningsstatistik
Familie	Regression model	Process / pipeline
Oprindelsesår	2012-2013	1983
Ophavsperson	Iacus, King & Porro (CEM foundation, 2012); subgroup HTE extensions by Imai & colleagues	Paul Rosenbaum and Donald Rubin
Type	Matching-based causal inference with subgroup CATE estimation	Method
Oprindelig kilde	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	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.
Aliasser	HTE-CEM, CEM with CATE estimation, subgroup CEM, coarsened exact matching with effect heterogeneity	PSM, propensity score weighting, covariate balance
Relaterede	5	3
Resumé	Heterogeneous treatment effect coarsened exact matching (HTE-CEM) extends the coarsened exact matching framework to estimate how treatment effects vary across subgroups or individual characteristics. After CEM creates balanced strata by coarsening continuous covariates into bins and exactly matching units within each bin, conditional average treatment effects (CATEs) are computed within or across these strata, revealing where treatment works, for whom, and by how much.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.