Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Pricing Engine: Estimating Causal Impacts in Real World Business
  Settings   We introduce the Pricing Engine package to enable the use of Double ML
estimation techniques in general panel data settings. Customization allows the
user to specify first-stage models, first-stage featurization, second stage
treatment selection and second stage causal-modeling. We also introduce a
DynamicDML class that allows the user to generate dynamic treatment-aware
forecasts at a range of leads and to understand how the forecasts will vary as
a function of causally estimated treatment parameters. The Pricing Engine is
built on Python 3.5 and can be run on an Azure ML Workbench environment with
the addition of only a few Python packages. This note provides high-level
discussion of the Double ML method, describes the packages intended use and
includes an example Jupyter notebook demonstrating application to some publicly
available data. Installation of the package and additional technical
documentation is available at
$\href{https://github.com/bquistorff/pricingengine}{github.com/bquistorff/pricingengine}$.

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The categories of this paper are: physics
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
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The categories of this paper are: ao-sci
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The categories of this paper are: atom-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th