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Sammenlign metoder

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Flerperioders kontrafaktisk konsekvensanalyse×Motfaktisk effektvurdering (CIE)×
FagfeltKausal inferensKausal inferens
FamilieRegression modelRegression model
Opprinnelsesår2000s–2010s1970s–2000s
OpphavspersonDeveloped through EU policy evaluation practice (European Commission); formalized by Lechner, Caliendo, and related econometriciansHeckman, Imbens, Rubin, and the program evaluation literature
TypeCausal inference / quasi-experimental evaluationCausal inference / program evaluation
Opprinnelig kildeCaliendo, M., & Kopeinig, S. (2008). Some Practical Guidance for the Implementation of Propensity Score Matching. Journal of Economic Surveys, 22(1), 31-72. DOI ↗Heckman, J. J., & Vytlacil, E. J. (2007). Econometric evaluation of social programs, Part I: Causal models, structural models and econometric policy evaluation. Handbook of Econometrics, 6B, 4779-4874. DOI ↗
Aliasmulti-period CIE, longitudinal counterfactual evaluation, dynamic counterfactual impact evaluation, multi-wave CIECIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation
Relaterte45
SammendragMulti-period Counterfactual Impact Evaluation (CIE) estimates the causal effect of a policy or program by constructing what would have happened to treated units across multiple time periods had they not been treated. Unlike single-period evaluations, it tracks treatment effects as they evolve over time, capturing dynamic, delayed, or fading impacts that a two-period comparison would miss.Counterfactual Impact Evaluation is a family of causal methods that estimates the effect of an intervention by comparing what actually happened to participants with what would have happened had the intervention not taken place. Formalised in the Rubin Causal Model and extended by Heckman, Imbens and others, CIE underlies most modern program and policy evaluation practice.
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ScholarGateSammenlign metoder: Multi-period Counterfactual Impact Evaluation · Counterfactual Impact Evaluation. Hentet 2026-06-20 fra https://scholargate.app/no/compare