[HTML][HTML] Causal inference on neuroimaging data with Mendelian randomisation
While population-scale neuroimaging studies offer the promise of discovery and
characterisation of subtle risk factors, massive sample sizes increase the power for both …
characterisation of subtle risk factors, massive sample sizes increase the power for both …
Statistical challenges of administrative and transaction data
DJ Hand - Journal of the Royal Statistical Society Series A …, 2018 - academic.oup.com
Administrative data are becoming increasingly important. They are typically the side effect of
some operational exercise and are often seen as having significant advantages over …
some operational exercise and are often seen as having significant advantages over …
Deep learning for cardiovascular risk stratification
DE Schlesinger, CM Stultz - Current Treatment Options in Cardiovascular …, 2020 - Springer
Purpose of review Although deep learning represents an exciting platform for the
development of risk stratification models, it is challenging to evaluate these models beyond …
development of risk stratification models, it is challenging to evaluate these models beyond …
[HTML][HTML] Introducing causal inference in the energy-efficient building design process
Abstract “What-if” questions are intuitively generated and commonly asked during the design
process. Engineers and architects need to inherently conduct design decisions, progressing …
process. Engineers and architects need to inherently conduct design decisions, progressing …
Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena
Interpretable machine learning (IML) is concerned with the behavior and the properties of
machine learning models. Scientists, however, are only interested in models as a gateway to …
machine learning models. Scientists, however, are only interested in models as a gateway to …
Causal generative neural networks
We present Causal Generative Neural Networks (CGNNs) to learn functional causal models
from observational data. CGNNs leverage conditional independencies and distributional …
from observational data. CGNNs leverage conditional independencies and distributional …
Valid inference after causal discovery
Causal discovery and causal effect estimation are two fundamental tasks in causal
inference. While many methods have been developed for each task individually, statistical …
inference. While many methods have been developed for each task individually, statistical …
Comparing high-dimensional confounder control methods for rapid cohort studies from electronic health records
Aims: Electronic health records (EHR), containing rich clinical histories of large patient
populations, can provide evidence for clinical decisions when evidence from trials and …
populations, can provide evidence for clinical decisions when evidence from trials and …
A practical guide to causal discovery with cohort data
In this guide, we present how to perform constraint-based causal discovery using three
popular software packages: pcalg (with add-ons tpc and micd), bnlearn, and TETRAD. We …
popular software packages: pcalg (with add-ons tpc and micd), bnlearn, and TETRAD. We …
Causal discovery of gene regulation with incomplete data
R Foraita, J Friemel, K Günther… - Journal of the Royal …, 2020 - academic.oup.com
Causal discovery algorithms aim to identify causal relations from observational data and
have become a popular tool for analysing genetic regulatory systems. In this work, we …
have become a popular tool for analysing genetic regulatory systems. In this work, we …