7,725 research outputs found
Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines
The dearth of prescribing guidelines for physicians is one key driver of the
current opioid epidemic in the United States. In this work, we analyze medical
and pharmaceutical claims data to draw insights on characteristics of patients
who are more prone to adverse outcomes after an initial synthetic opioid
prescription. Toward this end, we propose a generative model that allows
discovery from observational data of subgroups that demonstrate an enhanced or
diminished causal effect due to treatment. Our approach models these
sub-populations as a mixture distribution, using sparsity to enhance
interpretability, while jointly learning nonlinear predictors of the potential
outcomes to better adjust for confounding. The approach leads to
human-interpretable insights on discovered subgroups, improving the practical
utility for decision suppor
Symbolic regression of generative network models
Networks are a powerful abstraction with applicability to a variety of
scientific fields. Models explaining their morphology and growth processes
permit a wide range of phenomena to be more systematically analysed and
understood. At the same time, creating such models is often challenging and
requires insights that may be counter-intuitive. Yet there currently exists no
general method to arrive at better models. We have developed an approach to
automatically detect realistic decentralised network growth models from
empirical data, employing a machine learning technique inspired by natural
selection and defining a unified formalism to describe such models as computer
programs. As the proposed method is completely general and does not assume any
pre-existing models, it can be applied "out of the box" to any given network.
To validate our approach empirically, we systematically rediscover pre-defined
growth laws underlying several canonical network generation models and credible
laws for diverse real-world networks. We were able to find programs that are
simple enough to lead to an actual understanding of the mechanisms proposed,
namely for a simple brain and a social network
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