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pro vyhledávání: '"van der Zander, Benito"'
Learning the unknown causal parameters of a linear structural causal model is a fundamental task in causal analysis. The task, known as the problem of identification, asks to estimate the parameters of the model from a combination of assumptions on t
Externí odkaz:
http://arxiv.org/abs/2407.12528
The framework of Pearl's Causal Hierarchy (PCH) formalizes three types of reasoning: observational, interventional, and counterfactual, that reflect the progressive sophistication of human thought regarding causation. We investigate the computational
Externí odkaz:
http://arxiv.org/abs/2405.07373
To characterize the computational complexity of satisfiability problems for probabilistic and causal reasoning within the Pearl's Causal Hierarchy, arXiv:2305.09508 [cs.AI] introduce a new natural class, named succ-$\exists$R. This class can be viewe
Externí odkaz:
http://arxiv.org/abs/2405.04697
We study formal languages which are capable of fully expressing quantitative probabilistic reasoning and do-calculus reasoning for causal effects, from a computational complexity perspective. We focus on satisfiability problems whose instance formula
Externí odkaz:
http://arxiv.org/abs/2305.09508
Causal effect estimation from observational data is a fundamental task in empirical sciences. It becomes particularly challenging when unobserved confounders are involved in a system. This paper focuses on front-door adjustment -- a classic technique
Externí odkaz:
http://arxiv.org/abs/2211.16468
Linear structural equation models represent direct causal effects as directed edges and confounding factors as bidirected edges. An open problem is to identify the causal parameters from correlations between the nodes. We investigate models, whose di
Externí odkaz:
http://arxiv.org/abs/2203.01852
Publikováno v:
Artificial Intelligence 270 (2019) 1-40
Principled reasoning about the identifiability of causal effects from non-experimental data is an important application of graphical causal models. This paper focuses on effects that are identifiable by covariate adjustment, a commonly used estimatio
Externí odkaz:
http://arxiv.org/abs/1803.00116
Publikováno v:
In Artificial Intelligence May 2019 270:1-40
Publikováno v:
In Artificial Intelligence August 2023 321
Autor:
van der Zander, Benito, Liśkiewicz, Maciej, Textor, Johannes, Yang, Qiang, Wooldridge, Michael, Sub Theoretical Biology, Dep Biologie, Theoretical Biology and Bioinformatics
Publikováno v:
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015), 3243. AAAI Press
STARTPAGE=3243;TITLE=Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015)
STARTPAGE=3243;TITLE=Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015)
Instrumental variables (IVs) are widely used to identify causal effects. For this purpose IVs have to be exogenous, i.e., causally unrelated to all variables in the model except the explanatory variable X. It can be hard to find such variables. A gen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=narcis______::05c71cd8c1b6a13c42e71c2409110aef
https://dspace.library.uu.nl/handle/1874/324932
https://dspace.library.uu.nl/handle/1874/324932