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Kausale Mediationsanalyse (Natürliche Direkte und Indirekte Effekte)×Regression Discontinuity Design (RDD)×
FachgebietKausale InferenzKausale Inferenz
FamilieRegression modelRegression model
Entstehungsjahr20102008
UrheberPearl (2001); general framework by Imai, Keele & Tingley (2010)Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
TypCounterfactual causal decompositionQuasi-experimental causal design
Wegweisende QuellePearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420. link ↗Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗
Aliasnamennatural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediationRDD, regression discontinuity design, sharp RDD, fuzzy RDD
Verwandt55
ZusammenfassungCausal 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.
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ScholarGateMethoden vergleichen: Causal Mediation Analysis · Regression Discontinuity. Abgerufen am 2026-06-18 von https://scholargate.app/de/compare