arXiv 8 Jun 2018 · Econometrics · 1 citations (OpenAlex)
arXiv:1806.03285 · PDF · DOI · OpenAlex · Extracted main text
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 works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W.… (2017) Double/debiased machine learning for treatment and causal parameters | 0.737 | 3 | 2 | 100% |
| 2 | V. Chernozhukov, M. Goldman, V. Semenova, and M. Taddy, “Orthogonal… (2017) Orthogonal machine learning for demand estimation: High dimensional causal inference in dynamic panels | 0.511 | 2 | 1 | 100% |
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