Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Finding Regions of Heterogeneity in Decision-Making via Expected
  Conditional Covariance   Individuals often make different decisions when faced with the same context,
due to personal preferences and background. For instance, judges may vary in
their leniency towards certain drug-related offenses, and doctors may vary in
their preference for how to start treatment for certain types of patients. With
these examples in mind, we present an algorithm for identifying types of
contexts (e.g., types of cases or patients) with high inter-decision-maker
disagreement. We formalize this as a causal inference problem, seeking a region
where the assignment of decision-maker has a large causal effect on the
decision. Our algorithm finds such a region by maximizing an empirical
objective, and we give a generalization bound for its performance. In a
semi-synthetic experiment, we show that our algorithm recovers the correct
region of heterogeneity accurately compared to baselines. Finally, we apply our
algorithm to real-world healthcare datasets, recovering variation that aligns
with existing clinical knowledge.

3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an