Compară metode

	Coarsened Exact Matching pentru Efecte de Tratament Eterogene	Difference-in-Differences (Diff-in-Diff)
Domeniu	Inferență cauzală	Econometrie
Familie	Regression model	Regression model
Anul apariției	2012-2013	1994
Autorul original	Iacus, King & Porro (CEM foundation, 2012); subgroup HTE extensions by Imai & colleagues	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Tip	Matching-based causal inference with subgroup CATE estimation	Causal inference / panel regression
Sursa seminală	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Denumiri alternative	HTE-CEM, CEM with CATE estimation, subgroup CEM, coarsened exact matching with effect heterogeneity	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Înrudite	5	5
Rezumat	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.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.