Quantifying causal effects to enhance explainability in causal bayesian networks (2026)
Source: Knowledge-Based Systems. Unidade: EESC
Subjects: CAUSALIDADE, MODELOS PARA PROCESSOS ESTOCÁSTICOS, SISTEMAS DINÂMICOS, ENGENHARIA ELÉTRICA
ABNT
ARONE, Rafael Augusto Caracciolo e CAETANO, Henrique de Oliveira e MACIEL, Carlos Dias. Quantifying causal effects to enhance explainability in causal bayesian networks. Knowledge-Based Systems, v. 338, p. 1-17, 2026Tradução . . Disponível em: https://dx.doi.org/10.1016/j.knosys.2026.115495. Acesso em: 31 mar. 2026.APA
Arone, R. A. C., Caetano, H. de O., & Maciel, C. D. (2026). Quantifying causal effects to enhance explainability in causal bayesian networks. Knowledge-Based Systems, 338, 1-17. doi:10.1016/j.knosys.2026.115495NLM
Arone RAC, Caetano H de O, Maciel CD. Quantifying causal effects to enhance explainability in causal bayesian networks [Internet]. Knowledge-Based Systems. 2026 ; 338 1-17.[citado 2026 mar. 31 ] Available from: https://dx.doi.org/10.1016/j.knosys.2026.115495Vancouver
Arone RAC, Caetano H de O, Maciel CD. Quantifying causal effects to enhance explainability in causal bayesian networks [Internet]. Knowledge-Based Systems. 2026 ; 338 1-17.[citado 2026 mar. 31 ] Available from: https://dx.doi.org/10.1016/j.knosys.2026.115495
