Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Prototype-based Counterfactual Explanation for Causal Classification   Counterfactual explanation is one branch of interpretable machine learning
that produces a perturbation sample to change the model's original decision.
The generated samples can act as a recommendation for end-users to achieve
their desired outputs. Most of the current counterfactual explanation
approaches are the gradient-based method, which can only optimize the
differentiable loss functions with continuous variables. Accordingly, the
gradient-free methods are proposed to handle the categorical variables, which
however present several major limitations: 1) causal relationships among
features are typically ignored when generating the counterfactuals, possibly
resulting in impractical guidelines for decision-makers; 2) the generation of
the counterfactual sample is prohibitively slow and requires lots of parameter
tuning for combining different loss functions. In this work, we propose a
causal structure model to preserve the causal relationship underlying the
features of the counterfactual. In addition, we design a novel gradient-free
optimization based on the multi-objective genetic algorithm that generates the
counterfactual explanations for the mixed-type of continuous and categorical
data. Numerical experiments demonstrate that our method compares favorably with
state-of-the-art methods and therefore is applicable to any prediction model.
All the source code and data are available at
\textit{\url{{https://github.com/tridungduong16/multiobj-scm-cf}}}.

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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph