Confronta i metodi

	Regressione Discontinua Aumentata con Machine Learning	Abbinamento del punteggio di propensione
Campo	Inferenza causale	Statistica per la ricerca
Famiglia	Regression model	Process / pipeline
Anno di origine	2019	1983
Ideatore	Imbens & Wager (2019); Calonico, Cattaneo & Farrell (2019)	Paul Rosenbaum and Donald Rubin
Tipo	Causal inference / quasi-experimental	Method
Fonte seminale	Calonico, S., Cattaneo, M. D., & Farrell, M. H. (2019). Optimal mean squared error bandwidth selection for regression discontinuity designs. Bernoulli, 25(4A), 2703-2729.	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	ML-RDD, ML-augmented RD, data-adaptive RDD, nonparametric RDD with ML	PSM, propensity score weighting, covariate balance
Correlati	3	3
Sintesi	Machine learning-augmented regression discontinuity design (ML-RDD) combines the sharp identification logic of classical RDD — exploiting a known assignment cutoff in a running variable — with flexible, data-adaptive ML methods for bandwidth selection, conditional mean estimation, and covariate adjustment. The goal is to recover a more accurate and less assumption-laden estimate of the local average treatment effect at the threshold.	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.