Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: To Explain or to Predict?   Statistical modeling is a powerful tool for developing and testing theories
by way of causal explanation, prediction, and description. In many disciplines
there is near-exclusive use of statistical modeling for causal explanation and
the assumption that models with high explanatory power are inherently of high
predictive power. Conflation between explanation and prediction is common, yet
the distinction must be understood for progressing scientific knowledge. While
this distinction has been recognized in the philosophy of science, the
statistical literature lacks a thorough discussion of the many differences that
arise in the process of modeling for an explanatory versus a predictive goal.
The purpose of this article is to clarify the distinction between explanatory
and predictive modeling, to discuss its sources, and to reveal the practical
implications of the distinction to each step in the modeling process.

1
The categories of this paper are: cs
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg