Results 51 to 60 of about 353,766 (277)

Causality Without Causal Models

open access: yesElectronic Proceedings in Theoretical Computer Science
In Proceedings TARK 2025, arXiv:2511 ...
Halpern, Joseph Y., Pass, Rafael
openaire   +2 more sources

Sirolimus for Extracranial Arteriovenous Malformations: A Scoping Review of the Evidence in Syndromic and Non‐Syndromic Cases

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Arteriovenous malformations (AVMs) are rare, high‐flow, vascular anomalies that can occur either sporadically or as part of a genetic syndrome. AVMs can progress with serious morbidity and even mortality if left unchecked. Sirolimus is an mTOR inhibitor that is effective in low‐flow vascular malformations; however, its role in AVMs is unclear.
Will Swansson   +3 more
wiley   +1 more source

A Bayesian view of doubly robust causal inference [PDF]

open access: yes, 2016
In causal inference confounding may be controlled either through regression adjustment in an outcome model, or through propensity score adjustment or inverse probability of treatment weighting, or both. The latter approaches, which are based on modelling
Belzile, Léo R.   +2 more
core   +2 more sources

Evaluating the Utility of Paired Tumor and Germline Targeted DNA Sequencing for Pediatric Oncology Patients: A Single Institution Report

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Objective To evaluate the diagnostic yield and utility of universal paired tumor–normal multigene panel sequencing in newly diagnosed pediatric solid and central nervous system (CNS) tumor patients and to compare the detection of germline pathogenic/likely pathogenic variants (PV/LPVs) against established clinical referral criteria for cancer ...
Natalie Waligorski   +9 more
wiley   +1 more source

Causality and Causal Models: A Conceptual Perspective*

open access: yesInternational Statistical Review, 2006
SummaryThis paper aims at displaying a synthetic view of the historical development and the current research concerning causal relationships, starting from the Aristotelian doctrine of causes, following with the main philosophical streams until the middle of the twentieth century, and commenting on the present intensive research work in the statistical
openaire   +2 more sources

NRASQ61R Expression in Lymphatic Endothelial Cells Causes Enlarged Vessels, Hemorrhagic Chylous Effusions, and High Mortality in a Mouse Model of Kaposiform Lymphangiomatosis

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Background Kaposiform lymphangiomatosis (KLA) is an aggressive complex lymphatic anomaly. Patients exhibit malformed lymphatic vessels and often develop hemorrhagic effusions and elevated angiopoietin‐2 (Ang‐2) levels. A somatic NRAS p.Q61R (NRASQ61R) mutation has been associated with KLA.
C. Griffin McDaniel   +3 more
wiley   +1 more source

Models for Prediction, Explanation and Control: Recursive Bayesian Networks

open access: yesTheoria, 2011
The Recursive Bayesian Net (RBN) formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms.
Lorenzo Casini   +3 more
doaj   +1 more source

A Decidable Characterization of a Graphical Pi-calculus with Iterators

open access: yes, 2010
This paper presents the Pi-graphs, a visual paradigm for the modelling and verification of mobile systems. The language is a graphical variant of the Pi-calculus with iterators to express non-terminating behaviors.
Ahmed Rezine   +21 more
core   +4 more sources

Pulmonary Dysfunction Is Associated With Sleep Study Abnormalities in Children With Sickle Cell Disease: A Multicenter Study

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Introduction Pulmonary dysfunction and sleep abnormalities are common in children with sickle cell disease (SCD) and are associated with worse clinical outcomes. Whether spirometry abnormalities are associated with polysomnography (PSG) findings remains unclear.
Ammar Saadoon Alishlash   +4 more
wiley   +1 more source

Learning latent functions for causal discovery

open access: yesMachine Learning: Science and Technology, 2023
Causal discovery from observational data offers unique opportunities in many scientific disciplines: reconstructing causal drivers, testing causal hypotheses, and comparing and evaluating models for optimizing targeted interventions.
Emiliano Díaz   +3 more
doaj   +1 more source

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