Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Online Causal Structure Learning in the Presence of Latent Variables   We present two online causal structure learning algorithms which can track
changes in a causal structure and process data in a dynamic real-time manner.
Standard causal structure learning algorithms assume that causal structure does
not change during the data collection process, but in real-world scenarios, it
does often change. Therefore, it is inappropriate to handle such changes with
existing batch-learning approaches, and instead, a structure should be learned
in an online manner. The online causal structure learning algorithms we present
here can revise correlation values without reprocessing the entire dataset and
use an existing model to avoid relearning the causal links in the prior model,
which still fit data. Proposed algorithms are tested on synthetic and
real-world datasets, the latter being a seasonally adjusted commodity price
index dataset for the U.S. The online causal structure learning algorithms
outperformed standard FCI by a large margin in learning the changed causal
structure correctly and efficiently when latent variables were present.

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The categories of this paper are: chem-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
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The categories of this paper are: math
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The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org
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The categories of this paper are: math-ph