因果推理在Netflix的应用调查

At Netflix, we want to entertain the world through creating engaging content and helping members discover the titles they will love. Key to that is understanding causal effects that connect changes we make in the product to indicators of member joy.

在Netflix，我们希望通过创造有吸引力的内容和帮助会员发现他们会喜欢的作品来娱乐世界。这方面的关键是了解因果效应，将我们在产品中的变化与会员的快乐指标联系起来。

To measure causal effects we rely heavily on AB testing, but we also leverage quasi-experimentation in cases where AB testing is limited. Many scientists across Netflix have contributed to the way that Netflix analyzes these causal effects.

为了测量因果效应，我们主要依靠AB测试，但在AB测试有限的情况下，我们也会利用准实验的方法。Netflix的许多科学家对Netflix分析这些因果效应的方式做出了贡献。

To celebrate that impact and learn from each other, Netflix scientists recently came together for an internal Causal Inference and Experimentation Summit. The weeklong conference brought speakers from across the content, product, and member experience teams to learn about methodological developments and applications in estimating causal effects. We covered a wide range of topics including difference-in-difference estimation, double machine learning, Bayesian AB testing, and causal inference in recommender systems among many others.

为了庆祝这一影响并相互学习，Netflix的科学家们最近聚集在一起参加了内部因果推断和实验峰会。在为期一周的会议上，来自内容、产品和会员体验团队的演讲者了解了估计因果效应的方法学发展和应用。我们涵盖了广泛的主题，包括差额估计、双重机器学习、贝叶斯AB测试和推荐系统中的因果推断等等。

We are excited to share a sneak peek of the event with you in this blog post through selected examples of the talks, giving a behind the scenes look at our community and the breadth of causal inference at Netflix. We look forward to connecting with you through a future external event and additional blog posts!

我们很高兴在这篇博文中与你分享这次活动的偷窥，通过选定的会谈实例，让你了解我们社区的幕后情况和Netflix的因果推理的广度。我们期待着通过未来的外部活动和更多的博文与你联系起来

Yinghong Lan, Vinod Bakthavachalam, Lavanya Sharan, Marie Douriez, Bahar Azarnoush, Mason Kroll

兰英红,维诺德-巴卡瓦查拉姆,拉万亚-沙兰,玛丽-杜里兹,巴哈尔-阿扎努什,梅森-克罗尔

At Netflix, we are passionate about connecting our members with great stories that can come from anywhere, and be loved everywhere. In fact, we stream in more than 30 l...