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pro vyhledávání: '"Elahi, Muhammad Qasim"'
Causal discovery is a fundamental problem with applications spanning various areas in science and engineering. It is well understood that solely using observational data, one can only orient the causal graph up to its Markov equivalence class, necess
Externí odkaz:
http://arxiv.org/abs/2410.20089
Additive noise models (ANMs) are an important setting studied in causal inference. Most of the existing works on ANMs assume causal sufficiency, i.e., there are no unobserved confounders. This paper focuses on confounded ANMs, where a set of treatmen
Externí odkaz:
http://arxiv.org/abs/2407.10014
Causal discovery aims to uncover cause-and-effect relationships encoded in causal graphs by leveraging observational, interventional data, or their combination. The majority of existing causal discovery methods are developed assuming infinite interve
Externí odkaz:
http://arxiv.org/abs/2405.11548
Autor:
Elsaadany, Mazen, Elahi, Muhammad Qasim, AtaAllah, Faris, Rehman, Habibur, Mukhopadhyay, Shayok
Publikováno v:
Frontiers in Control Engineering; 9/16/2022, p1-16, 16p
