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University of Bristol6d
Causal Inference in Epidemiology: Concepts and Methods
This course defines causation in biomedical research, describes how emulating a ‘target trial’ can clarify the question being addressed and guide analysis choices, introduces methods to make causal ...
University of Bristol6d
Causal Inference in Epidemiology: Concepts and Methods
This course defines causation in biomedical research, describes how emulating a ‘target trial’ can clarify the question being addressed and guide analysis choices, introduces methods to make causal ...
eLife5d
Assemblies, synapse clustering, and network topology interact with plasticity to explain structure-function relationships of the cortical connectome
This useful study presents a biologically realistic, large-scale cortical model of the rat's non-barrel somatosensory cortex, investigating synaptic plasticity of excitatory connections under varying ...
IEEE23h
Meta-Dag: Meta Causal Discovery Via Bilevel Optimization
Causal discovery in the form of directed acyclic graph (DAG) structure learning finds causal relationships among features of sampled data and has been recognized as one of the most important problems ...
IEEE3d
Dynamic Causal Graph Convolutional Network for Traffic Prediction
Modeling complex spatiotemporal dependencies in correlated traffic series is essential for traffic prediction. While recent works have shown improved prediction performance by using neural networks to ...
thorax.bmj5d
Estimating individual treatment effects on COPD exacerbations by causal machine learning on randomised controlled trials - Thorax
Rationale Estimating the causal effect of an intervention at individual level, also called individual treatment effect (ITE), may help in identifying response prior to the intervention. Objectives We ...