发帖
雷达卡
摘要翻译：
我们介绍了定价引擎包，以支持在一般面板数据设置中使用双ML估计技术。定制允许用户指定第一阶段模型、第一阶段特征化、第二阶段处理选择和第二阶段因果建模。我们还引入了一个DynamicDML类，它允许用户在一系列线索上生成动态的治疗感知预测，并理解预测如何随着因果估计的治疗参数而变化。定价引擎构建在Python 3.5上，只需添加几个Python包就可以在Azure ML工作台环境中运行。本说明提供了Double ML方法的高级讨论，描述了包的预期用途，并包括一个示例Jupyter笔记本，演示了对一些公开可用数据的应用。在$\href{https://github.com/bquistorff/pricingengine}{github.com/bquistorff/pricingengine}可获得该包的安装和其他技术文档。
---
英文标题：
《Pricing Engine: Estimating Causal Impacts in Real World Business
Settings》
---
作者：
Matt Goldman, Brian Quistorff
---
最新提交年份：
2018
---
分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
--
一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
--
---
英文摘要：
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 $\href{https://github.com/bquistorff/pricingengine}{github.com/bquistorff/pricingengine}$.
---
PDF链接：
https://arxiv.org/pdf/1806.03285
京公网安备 11010802022788号
