Compară metode

	Estimarea Spațială Dublu Robustă	Difference-in-Differences (Diff-in-Diff)
Domeniu	Inferență cauzală	Econometrie
Familie	Regression model	Regression model
Anul apariției	2010s–2020s	1994
Autorul original	Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Tip	Semiparametric causal estimator	Causal inference / panel regression
Sursa seminală	Papadogeorgou, G., Mealli, F., & Zigler, C. M. (2019). Causal inference with interfering units for cluster and population level treatment allocation programs. Biometrics, 75(3), 778-787.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Denumiri alternative	Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Înrudite	5	5
Rezumat	Spatial doubly robust estimation is a semiparametric causal inference method that combines propensity score weighting with outcome regression modeling — providing protection against misspecification of either component — while explicitly accounting for spatial autocorrelation among units. It extends the classical augmented inverse probability weighting (AIPW) estimator to settings where treatment assignment and outcomes are geographically clustered or spatially dependent.	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.