Linganisha mbinu

	Spatial Coarsened Exact Matching (Spatial CEM)	Tofauti-katika-Tofauti (Diff-in-Diff)
Nyanja	Uhitimisho wa Kisababishi	Ekonometriki
Familia	Regression model	Regression model
Mwaka wa asili	2012 (CEM foundation); spatial extension in applied literature 2015-present	1994
Mwanzilishi	Iacus, King & Porro (CEM foundation, 2012); extended to spatial contexts by applied spatial econometricians	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Aina	Quasi-experimental matching estimator with spatial covariates	Causal inference / panel regression
Chanzo asilia	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
Majina mbadala	Spatial CEM, Geographic CEM, Spatial exact matching, CEM with spatial covariates	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Zinazohusiana	6	5
Muhtasari	Spatial Coarsened Exact Matching applies the Coarsened Exact Matching framework to study designs involving geographic units — neighbourhoods, census tracts, municipalities, or grid cells. Covariates are coarsened into discrete bins and units are matched exactly on those bins, with spatial attributes (location, adjacency, geographic characteristics) incorporated as matching dimensions to control for spatial confounding.	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.