# Engineering for a Science-Centric Experimentation Platform

arXiv: https://arxiv.org/abs/1910.03878 · HTML: https://mlsanity.com/paper/1910.03878/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testing](https://mlsanity.com/paper/2308.09790/) (2023)
- [Causal Reasoning in Software Quality Assurance: A Systematic Review](https://mlsanity.com/paper/2408.17183/) (2024)
- [You Only Compress Once: Optimal Data Compression for Estimating Linear Models](https://mlsanity.com/paper/2102.11297/) (2021)
- [Practical Policy Optimization with Personalized Experimentation](https://mlsanity.com/paper/2303.17648/) (2023)
- [AExGym: Benchmarks and Environments for Adaptive Experimentation](https://mlsanity.com/paper/2408.04531/) (2024)
- [Towards Measuring Sell Side Outcomes in Buy Side Marketplace Experiments using In-Experiment Bipartite Graph](https://mlsanity.com/paper/2409.04174/) (2024)

## Abstract (arXiv)

Netflix is an internet entertainment service that routinely employs experimentation to guide strategy around product innovations. As Netflix grew, it had the opportunity to explore increasingly specialized improvements to its service, which generated demand for deeper analyses supported by richer metrics and powered by more diverse statistical methodologies. To facilitate this, and more fully harness the skill sets of both engineering and data science, Netflix engineers created a science-centric experimentation platform that leverages the expertise of data scientists from a wide range of backgrounds by allowing them to make direct code contributions in the languages used by scientists (Python and R). Moreover, the same code that runs in production is able to be run locally, making it straightforward to explore and graduate both metrics and causal inference methodologies directly into production services. In this paper, we utilize a case-study research method to provide two main contributions. Firstly, we report on the architecture of this platform, with a special emphasis on its novel aspects: how it supports science-centric end-to-end workflows without compromising engineering requirements. Secondly, we describe its approach to causal inference, which leverages the potential outcomes conceptual framework to provide a unified abstraction layer for arbitrary statistical models and methodologies.