Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Harmonization with Flow-based Causal Inference   Heterogeneity in medical data, e.g., from data collected at different sites
and with different protocols in a clinical study, is a fundamental hurdle for
accurate prediction using machine learning models, as such models often fail to
generalize well. This paper leverages a recently proposed
normalizing-flow-based method to perform counterfactual inference upon a
structural causal model (SCM), in order to achieve harmonization of such data.
A causal model is used to model observed effects (brain magnetic resonance
imaging data) that result from known confounders (site, gender and age) and
exogenous noise variables. Our formulation exploits the bijection induced by
flow for the purpose of harmonization. We infer the posterior of exogenous
variables, intervene on observations, and draw samples from the resultant SCM
to obtain counterfactuals. This approach is evaluated extensively on multiple,
large, real-world medical datasets and displayed better cross-domain
generalization compared to state-of-the-art algorithms. Further experiments
that evaluate the quality of confounder-independent data generated by our model
using regression and classification tasks are provided.

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The categories of this paper are: atom-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: astro-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: econ
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: nlin
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The categories of this paper are: funct-an