Pricing Engine: Estimating Causal Impacts in Real World Business Settings

TL;DR

The paper introduces the Pricing Engine package that applies Double ML estimation techniques to estimate causal impacts in real-world business panel data, enabling dynamic treatment-aware forecasting and customization.

Contribution

It presents a new Python package implementing Double ML methods for causal inference in panel data, including dynamic treatment forecasting capabilities.

Findings

Enables causal impact estimation in business data

Supports dynamic treatment-aware forecasts

Provides customizable modeling options

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