Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Overlapping Representations for the Estimation of
  Individualized Treatment Effects   The choice of making an intervention depends on its potential benefit or harm
in comparison to alternatives. Estimating the likely outcome of alternatives
from observational data is a challenging problem as all outcomes are never
observed, and selection bias precludes the direct comparison of differently
intervened groups. Despite their empirical success, we show that algorithms
that learn domain-invariant representations of inputs (on which to make
predictions) are often inappropriate, and develop generalization bounds that
demonstrate the dependence on domain overlap and highlight the need for
invertible latent maps. Based on these results, we develop a deep kernel
regression algorithm and posterior regularization framework that substantially
outperforms the state-of-the-art on a variety of benchmarks data sets.

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The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas