So sánh phương pháp

	Ghép Chính xác Tinh giản Không gian (Spatial CEM)	Phương pháp Sai phân kép (Difference-in-Differences - DiD)
Lĩnh vực	Suy luận nhân quả	Kinh tế lượng
Họ	Regression model	Regression model
Năm ra đời	2012 (CEM foundation); spatial extension in applied literature 2015-present	1994
Người khởi xướng	Iacus, King & Porro (CEM foundation, 2012); extended to spatial contexts by applied spatial econometricians	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Loại	Quasi-experimental matching estimator with spatial covariates	Causal inference / panel regression
Công trình gốc	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
Tên gọi khác	Spatial CEM, Geographic CEM, Spatial exact matching, CEM with spatial covariates	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Liên quan	6	5
Tóm tắt	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.