Pricing Engine: Estimating Causal Impacts in Real World Business Settings
This work provides a tool for practitioners in business settings to estimate causal impacts, but it is incremental as it packages existing methods into a software library.
The authors introduced the Pricing Engine package to apply Double ML estimation techniques in panel data settings, enabling customizable causal modeling and dynamic treatment-aware forecasts, with an example provided using publicly available data.
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 $\href{https://github.com/bquistorff/pricingengine}{github.com/bquistorff/pricingengine}$.