So sánh phương pháp

	Trọng số xác suất nghịch đảo không gian (Spatial IPW)	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	2010s	1994
Người khởi xướng	Extension of Rosenbaum & Rubin (1983) IPW to spatial settings; formal treatment by Papadogeorgou et al. (2019)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Loại	Quasi-experimental / causal inference	Causal inference / panel regression
Công trình gốc	Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Tên gọi khác	Spatial IPW, Geographic IPW, Spatially-weighted IPW, SIPW	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Liên quan	6	5
Tóm tắt	Spatial Inverse Probability Weighting extends the classical IPW estimator to settings where units are geo-referenced and spatial location is a confounding dimension. By incorporating geographic coordinates or spatial proximity into the propensity score model, it reweights the observed sample so that treatment and control groups are balanced not only on measured covariates but also on spatial structure, enabling credible causal inference from spatially indexed observational data.	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.