Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Generalized Method of Moments   We provide an approach for learning deep neural net representations of models
described via conditional moment restrictions. Conditional moment restrictions
are widely used, as they are the language by which social scientists describe
the assumptions they make to enable causal inference. We formulate the problem
of estimating the underling model as a zero-sum game between a modeler and an
adversary and apply adversarial training. Our approach is similar in nature to
Generative Adversarial Networks (GAN), though here the modeler is learning a
representation of a function that satisfies a continuum of moment conditions
and the adversary is identifying violating moments. We outline ways of
constructing effective adversaries in practice, including kernels centered by
k-means clustering, and random forests. We examine the practical performance of
our approach in the setting of non-parametric instrumental variable regression.

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The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
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The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin