Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Identificação Causal com Grafos Acíclicos Direcionados (cálculo-do)× | Propensity Score Matching× | |
|---|---|---|
| Área≠ | Inferência causal | Estatística para pesquisa |
| Família≠ | Regression model | Process / pipeline |
| Ano de origem≠ | 2009 | 1983 |
| Autor original≠ | Judea Pearl | Paul Rosenbaum and Donald Rubin |
| Tipo≠ | Causal identification framework | Method |
| Fonte seminal≠ | Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606 | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. DOI ↗ |
| Outros nomes≠ | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) | PSM, propensity score weighting, covariate balance |
| Relacionados≠ | 5 | 3 |
| Resumo≠ | DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and backdoor paths. | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
| ScholarGateConjunto de dados ↗ |
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