Pricing Engine: Estimating Causal Impacts in Real World Business Settings

Matt Goldman and Brian Quistorff

Papers from arXiv.org

Abstract: 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 $\href{https://github.com/bquistorff/pricingengine}{github.com/bquistorff/pricingengine}$.

Date: 2018-06, Revised 2018-06
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/1806.03285 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1806.03285

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().