AIMC Topic: Causality
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Machine Learning for Causal Inference: On the Use of Cross-fit Estimators.
Epidemiology (Cambridge, Mass.)
May 1, 2021
BACKGROUND: Modern causal inference methods allow machine learning to be used to weaken parametric modeling assumptions. However, the use of machine learning may result in complications for inference. Doubly robust cross-fit estimators have been prop...
Commentary: Towards machine learning-enabled epidemiology.
International journal of epidemiology
Jan 23, 2021
Reflection on modern methods: generalized linear models for prognosis and intervention-theory, practice and implications for machine learning.
International journal of epidemiology
Jan 23, 2021
Prediction and causal explanation are fundamentally distinct tasks of data analysis. In health applications, this difference can be understood in terms of the difference between prognosis (prediction) and prevention/treatment (causal explanation). Ne...
Reflection on modern methods: when worlds collide-prediction, machine learning and causal inference.
International journal of epidemiology
Jan 23, 2021
Causal inference requires theory and prior knowledge to structure analyses, and is not usually thought of as an arena for the application of prediction modelling. However, contemporary causal inference methods, premised on counterfactual or potential...
Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.
Nucleic acids research
Jan 11, 2021
Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise ...
Epigenome
Datasets as Topic
Transcription Factors
Causality
Binding Sites
Linkage Disequilibrium
Genetic Diseases, Inborn
Organ Specificity
Molecular Sequence Annotation
Genome-Wide Association Study
Polymorphism, Single Nucleotide
Epigenomics
Models, Genetic
Humans
Deep Learning
Chromatin Immunoprecipitation
Histone Code
Can we learn individual-level treatment policies from clinical data?
Biostatistics (Oxford, England)
Apr 1, 2020
Regulatory oversight, causal inference, and safe and effective health care machine learning.
Biostatistics (Oxford, England)
Apr 1, 2020
In recent years, the applications of Machine Learning (ML) in the health care delivery setting have grown to become both abundant and compelling. Regulators have taken notice of these developments and the U.S. Food and Drug Administration (FDA) has b...
From development to deployment: dataset shift, causality, and shift-stable models in health AI.
Biostatistics (Oxford, England)
Apr 1, 2020
Machine learning in the estimation of causal effects: targeted minimum loss-based estimation and double/debiased machine learning.
Biostatistics (Oxford, England)
Apr 1, 2020
In recent decades, the fields of statistical and machine learning have seen a revolution in the development of data-adaptive regression methods that have optimal performance under flexible, sometimes minimal, assumptions on the true regression functi...
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