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Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.

Propensity Score Matching para Efeitos Heterogêneos de Tratamento×Diferenças em Diferenças (DiD)×
ÁreaInferência causalEconometria
FamíliaRegression modelRegression model
Ano de origem1983–20161994
Autor originalRosenbaum & Rubin (PSM foundation, 1983); Athey & Imbens (HTE extensions, 2016)Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
TipoCausal inference / matching with effect heterogeneityCausal inference / panel regression
Fonte seminalAthey, S., & Imbens, G. W. (2016). Recursive Partitioning for Heterogeneous Causal Effects. Proceedings of the National Academy of Sciences, 113(27), 7353-7360. DOI ↗Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Outros nomesHTE-PSM, CATE via PSM, subgroup treatment effect matching, conditional average treatment effect matchingdiff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Relacionados55
ResumoHeterogeneous Treatment Effect Propensity Score Matching extends standard PSM to estimate how treatment effects vary across subgroups or individual characteristics. Rather than reporting a single average treatment effect, it uses the matched sample to estimate conditional average treatment effects (CATE), revealing which types of units benefit most or least from a treatment.Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.
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ScholarGateComparar métodos: Heterogeneous Treatment Effect Propensity Score Matching · Difference-in-Differences. Recuperado em 2026-06-18 de https://scholargate.app/pt/compare