Compara mètodes

	Disseny de Discontinuïtat de Regressió Fuzzy	Emparellament per puntuació de propensió
Camp	Inferència causal	Estadística per a la recerca
Família	Regression model	Process / pipeline
Any d'origen	2001	1983
Autor original	Hahn, Todd & van der Klaauw	Paul Rosenbaum and Donald Rubin
Tipus	Quasi-experimental causal inference	Method
Font seminal	Hahn, J., Todd, P., & van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209.	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.
Àlies	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD	PSM, propensity score weighting, covariate balance
Relacionats	5	3
Resum	Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates causal effects when eligibility for a treatment is determined by a threshold on a running variable but actual take-up of that treatment is imperfect — some eligible units do not receive treatment and some ineligible units do. The cutoff acts as an instrument, and the estimand is a Local Average Treatment Effect (LATE) for compliers near 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.