Zobrazeno 1 - 10
of 805
pro vyhledávání: '"Liskiewicz, A."'
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
In observational studies, the true causal model is typically unknown and needs to be estimated from available observational and limited experimental data. In such cases, the learned causal model is commonly represented as a partially directed acyclic
Externí odkaz:
http://arxiv.org/abs/2302.14386
Enumerating the directed acyclic graphs (DAGs) of a Markov equivalence class (MEC) is an important primitive in causal analysis. The central resource from the perspective of computational complexity is the delay, that is, the time an algorithm that l
Externí odkaz:
http://arxiv.org/abs/2301.12212
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
Autor:
Akindehin, Seun, Liskiewicz, Arkadiusz, Liskiewicz, Daniela, Bernecker, Miriam, Garcia-Caceres, Cristina, Drucker, Daniel J., Finan, Brian, Grandl, Gerald, Gutgesell, Robert, Hofmann, Susanna M., Khalil, Ahmed, Liu, Xue, Cota, Perla, Bakhti, Mostafa, Czarnecki, Oliver, Bastidas-Ponce, Aimée, Lickert, Heiko, Kang, Lingru, Maity, Gandhari, Novikoff, Aaron, Parlee, Sebastian, Pathak, Ekta, Schriever, Sonja C., Sterr, Michael, Ussar, Siegfried, Zhang, Qian, DiMarchi, Richard, Tschöp, Matthias H., Pfluger, Paul T., Douros, Jonathan D., Müller, Timo D.
Publikováno v:
In Molecular Metabolism May 2024 83
Autor:
Seun Akindehin, Arkadiusz Liskiewicz, Daniela Liskiewicz, Miriam Bernecker, Cristina Garcia-Caceres, Daniel J. Drucker, Brian Finan, Gerald Grandl, Robert Gutgesell, Susanna M. Hofmann, Ahmed Khalil, Xue Liu, Perla Cota, Mostafa Bakhti, Oliver Czarnecki, Aimée Bastidas-Ponce, Heiko Lickert, Lingru Kang, Gandhari Maity, Aaron Novikoff, Sebastian Parlee, Ekta Pathak, Sonja C. Schriever, Michael Sterr, Siegfried Ussar, Qian Zhang, Richard DiMarchi, Matthias H. Tschöp, Paul T. Pfluger, Jonathan D. Douros, Timo D. Müller
Publikováno v:
Molecular Metabolism, Vol 83, Iss , Pp 101915- (2024)
Objective: The glucose-dependent insulinotropic polypeptide (GIP) decreases body weight via central GIP receptor (GIPR) signaling, but the underlying mechanisms remain largely unknown. Here, we assessed whether GIP regulates body weight and glucose c
Externí odkaz:
https://doaj.org/article/4df457ac59ce49e8af09680efdd590e2
Publikováno v:
Journal of Machine Learning Research 24(213):1-45, 2023
Counting and sampling directed acyclic graphs from a Markov equivalence class are fundamental tasks in graphical causal analysis. In this paper we show that these tasks can be performed in polynomial time, solving a long-standing open problem in this
Externí odkaz:
http://arxiv.org/abs/2205.02654