Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Simpson's Paradox to Discover Interesting Patterns in Behavioral
  Data   We describe a data-driven discovery method that leverages Simpson's paradox
to uncover interesting patterns in behavioral data. Our method systematically
disaggregates data to identify subgroups within a population whose behavior
deviates significantly from the rest of the population. Given an outcome of
interest and a set of covariates, the method follows three steps. First, it
disaggregates data into subgroups, by conditioning on a particular covariate,
so as minimize the variation of the outcome within the subgroups. Next, it
models the outcome as a linear function of another covariate, both in the
subgroups and in the aggregate data. Finally, it compares trends to identify
disaggregations that produce subgroups with different behaviors from the
aggregate. We illustrate the method by applying it to three real-world
behavioral datasets, including Q\&A site Stack Exchange and online learning
platforms Khan Academy and Duolingo.

13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom