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En el análisis estadístico de los estudios observacionales, el pareamiento por puntaje de propensión o Propensity score matching (PSM) en inglés, es una técnica estadística de coincidencia que intenta estimar el efecto de un tratamiento, 1 una política, 2 u otra intervención por cuenta de las covariables que predicen que recibe el ...
In the statistical analysis of observational data, propensity score matching (PSM) is a statistical matching technique that attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict receiving the treatment.
4 Propensity Score Matching | Métodos de evaluación de impacto. 4.1 Supuestos de identificación. Para estimar un efecto causal cuando hay un factor de confusión, necesitamos (1) supuesto de independencia condicional -CIA y (2) que la probabilidad de tratamiento esté entre 0 y 1 para cada estrato. Más formalmente,
17 de feb. de 2023 · Propensity score matching is a non-experimental causal inference technique that attempts to balance the treatment groups on confounding factors to make them comparable. This allows users to draw conclusions about the causal impact of a treatment on the outcome using observational data.
1 de feb. de 2022 · Propensity score matching (PSM) is a valuable way to control for bias and achieve pseudo-randomization in retrospective observation studies. The purpose of this review is to 1) provide a clear conceptual framework for PSM, 2) recommend how to best report its use in studies, and 3) offer some practical examples of implementation.
16 de may. de 2020 · Within this approach, propensity score matching (PSM) has been empirically proven, with outstanding performances across observational datasets. While PSM tutorials are available in the literature, there is still room for improvement.
22 de oct. de 2021 · Learn how to use propensity score matching (PSM) to create matched comparison samples for assessing the impacts of treatments or interventions in criminology and criminal justice. PSM is a statistical approach that adjusts for selection bias based on observed characteristics of the sample.
