Compara mètodes

	Anàlisi de processos condicionals (mediació moderada)	Anàlisi de mediació causal (efectes directes i indirectes naturals)	Disseny de Regressió per Discontinuïtat (RDD)
Camp	Inferència causal	Inferència causal	Inferència causal
Família	Regression model	Regression model	Regression model
Any d'origen	2018	2010	2008
Autor original	Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)	Pearl (2001); general framework by Imai, Keele & Tingley (2010)	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Tipus	Regression-based conditional process model	Counterfactual causal decomposition	Quasi-experimental causal design
Font seminal	Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2nd ed.). The Guilford Press. ISBN: 978-1462534654	Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420.	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
Àlies	moderated mediation, moderated mediation analysis, PROCESS model, Hayes PROCESS conditional process model	natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Relacionats	5	5	5
Resum	Conditional process analysis is Andrew F. Hayes's regression-based PROCESS framework (2018) that combines mediation and moderation in a single model, testing how an indirect effect changes across levels of a moderator. It quantifies conditional indirect and conditional direct effects and tests them with bootstrap confidence intervals.	Causal mediation analysis is a counterfactual framework that splits a treatment's total effect into a Natural Direct Effect (NDE) and a Natural Indirect Effect (NIE) that runs through a mediator. The modern general approach was formalised by Pearl (2001) and Imai, Keele and Tingley (2010), giving the decomposition a precise causal interpretation.	Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.