Comparer des méthodes

	Pondération par l'Inverse de la Probabilité Spatiale (Spatial IPW)	Différence-en-différences (Diff-in-Diff)
Domaine	Inférence causale	Économétrie
Famille	Regression model	Regression model
Année d'origine	2010s	1994
Auteur d'origine	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)
Type	Quasi-experimental / causal inference	Causal inference / panel regression
Source fondatrice	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
Alias	Spatial IPW, Geographic IPW, Spatially-weighted IPW, SIPW	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Apparentées	6	5
Résumé	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.