Comparar métodos

	Análise de Processo Condicional (Mediação Moderada)	Análise de Mediação Causal (Efeitos Diretos e Indiretos Naturais)	Desenho de Regressão por Descontinuidade (RDD)
Área	Inferência causal	Inferência causal	Inferência causal
Família	Regression model	Regression model	Regression model
Ano de origem	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)
Tipo	Regression-based conditional process model	Counterfactual causal decomposition	Quasi-experimental causal design
Fonte 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.
Outros nomes	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
Relacionados	5	5	5
Resumo	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.