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Causal Learning From Predictive Modeling for Observational Data
Frontiers
We consider the problem of learning structured causal models from observational data. In this work, we use causal Bayesian networks to represent causal...
2 months ago
Chat with Your Dataset using Bayesian Inferences.
Towards Data Science
The ability to ask questions to your data set has always been an intriguing prospect. You will be surprised how easy it is to learn a local...
10 months ago
Learning complex dependency structure of gene regulatory networks from high dimensional microarray data with Gaussian Bayesian networks
Nature
Reconstruction of Gene Regulatory Networks (GRNs) of gene expression data with Probabilistic Network Models (PNMs) is an open problem.
22 months ago
Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences
Science | AAAS
This paper provides a comprehensive, systematic meta-mapping of research questions in the social and health sciences to appropriate ML approaches.
22 months ago
Bayesian belief network modelling of household food security in rural South Africa
BMC Public Health
Achieving food security remains a key challenge for public policy throughout the world. As such, understanding the determinants of food...
39 months ago
A review of some techniques for inclusion of domain-knowledge into deep neural networks
Nature
We present a survey of ways in which existing scientific knowledge are included when constructing models with neural networks.
31 months ago