Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: BayesIMP: Uncertainty Quantification for Causal Data Fusion   While causal models are becoming one of the mainstays of machine learning,
the problem of uncertainty quantification in causal inference remains
challenging. In this paper, we study the causal data fusion problem, where
datasets pertaining to multiple causal graphs are combined to estimate the
average treatment effect of a target variable. As data arises from multiple
sources and can vary in quality and quantity, principled uncertainty
quantification becomes essential. To that end, we introduce Bayesian
Interventional Mean Processes, a framework which combines ideas from
probabilistic integration and kernel mean embeddings to represent
interventional distributions in the reproducing kernel Hilbert space, while
taking into account the uncertainty within each causal graph. To demonstrate
the utility of our uncertainty estimation, we apply our method to the Causal
Bayesian Optimisation task and show improvements over state-of-the-art methods.

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The categories of this paper are: q-fin
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The categories of this paper are: econ
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
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The categories of this paper are: hep-th
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The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph