Confronta i metodi

	Disegno di Regressione con Discontinuità Spaziale (Spatial RDD)	Abbinamento del punteggio di propensione
Campo	Inferenza causale	Statistica per la ricerca
Famiglia	Regression model	Process / pipeline
Anno di origine	2010s	1983
Ideatore	Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)	Paul Rosenbaum and Donald Rubin
Tipo	Quasi-experimental causal inference	Method
Fonte seminale	Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Alias	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design	PSM, propensity score weighting, covariate balance
Correlati	4	3
Sintesi	Spatial Regression Discontinuity Design uses a geographic or administrative boundary as the threshold that assigns units to treatment. Observations just inside one side of the boundary are compared with those just outside it, exploiting the near-random variation in treatment status near the cutoff to recover a local causal effect. The approach is widely used in economics, political science, and public health when policies or institutions change sharply at a border.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.