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arXiv · 1910.01305

Efficient Computation of Linear Model Treatment Effects in an Experimentation Platform

Jeffrey Wong·Randall Lewis·Matthew Wardrop
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Abstract

Linear models are a core component for statistical software that analyzes treatment effects. They are used in experimentation platforms where analysis is automated, as well as scientific studies where analysis is done locally and manually. However, the implementations of linear models for online platforms and for local analysis are often different due to differing demands in scalability. To improve leverage, we developed one library that powers both modes of analysis, consolidates many forms of treatment effects from linear models, and optimizes the computational performance of the software. The highly performant algorithms allow the library to run linear models at Netflix's scale and reply to live queries for treatment effects from an online service without using distributed systems in seconds. Wrapping these algorithms in a library that can be used interactively make large scale linear models accessible for both online platforms and scientists to use locally.

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Jeffrey Wong, Randall Lewis, Matthew Wardrop. 2019-10-03. Efficient Computation of Linear Model Treatment Effects in an Experimentation Platform. https://arxiv.org/abs/1910.01305

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