Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Causal de Finetti: On the Identification of Invariant Causal Structure
  in Exchangeable Data   Learning invariant causal structure often relies on conditional independence
testing and assumption of independent and identically distributed data. Recent
work has explored inferring invariant causal structure using data coming from
different environments. These approaches are based on independent causal
mechanism (ICM) principle which postulates that the cause mechanism is
independent of the effect given cause mechanism. Despite its wide application
in machine learning and causal inference, there lacks a statistical
formalization of what independent mechanism means. Here we present Causal de
Finetti which offers a first statistical formalization of ICM principle.

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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys
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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: adap-org
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The categories of this paper are: q-bio
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The categories of this paper are: eess
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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: supr-con
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The categories of this paper are: hep-lat