70 research outputs found
Joint Structure Learning of Multiple Non-Exchangeable Networks
Several methods have recently been developed for joint structure learning of
multiple (related) graphical models or networks. These methods treat individual
networks as exchangeable, such that each pair of networks are equally
encouraged to have similar structures. However, in many practical applications,
exchangeability in this sense may not hold, as some pairs of networks may be
more closely related than others, for example due to group and sub-group
structure in the data. Here we present a novel Bayesian formulation that
generalises joint structure learning beyond the exchangeable case. In addition
to a general framework for joint learning, we (i) provide a novel default prior
over the joint structure space that requires no user input; (ii) allow for
latent networks; (iii) give an efficient, exact algorithm for the case of time
series data and dynamic Bayesian networks. We present empirical results on
non-exchangeable populations, including a real data example from biology, where
cell-line-specific networks are related according to genomic features.Comment: To appear in Proceedings of the Seventeenth International Conference
on Artificial Intelligence and Statistics (AISTATS
Deep Learning of Causal Structures in High Dimensions
Recent years have seen rapid progress at the intersection between causality
and machine learning. Motivated by scientific applications involving
high-dimensional data, in particular in biomedicine, we propose a deep neural
architecture for learning causal relationships between variables from a
combination of empirical data and prior causal knowledge. We combine
convolutional and graph neural networks within a causal risk framework to
provide a flexible and scalable approach. Empirical results include linear and
nonlinear simulations (where the underlying causal structures are known and can
be directly compared against), as well as a real biological example where the
models are applied to high-dimensional molecular data and their output compared
against entirely unseen validation experiments. These results demonstrate the
feasibility of using deep learning approaches to learn causal networks in
large-scale problems spanning thousands of variables
Happiness as a Driver of Risk-Avoiding Behavior
Understanding the reasons why individuals take risks, particularly unnecessary risks, remains an important question in economics. We provide the first evidence of a powerful connection between happiness and risk-avoidance. Using data on 300,000 Americans, we demonstrate that happier individuals wear seatbelts more frequently. This result is obtained with five different methodological approaches, including Bayesian model-selection and an instrumented analysis based on unhappiness through widowhood. Independent longitudinal data corroborate the finding, showing that happiness is predictive of future motor vehicle accidents. Our results are consistent with a rational-choice explanation: happy people value life and thus act to preserve it.risk preferences, seatbelt usage, vehicle accidents, subjective well-being, happiness
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