Improve Your Next Experiment by Learning Better Proxy Metrics From Past Experiments

Airbnb at KDD 2023

Accelerating our A/B experiments with machine learning

Co-authors: Kenneth Tay and Xiaofeng Wang At Linkedin, we constantly evaluate the value our products and services deliver, so that we can provide the best possible experiences for our members and customers. This includes understanding how product changes impact key metrics related to those experiences. However, simply looking at connections between product changes and key metrics can be misleading. As we know, correlation does not always imply causation. When making decisions about the path forward for a product or feature, we need to know the causal impact of that change on our key […]

Ocelot: Scaling observational causal inference at LinkedIn

We are constantly striving to improve the experience on LinkedIn for our members and customers, with research and experimentation, such as A/B Testing, playing a key role in that work. Nearly a decade ago, I discussed the importance of these techniques in our journey to create economic opportunity for every member of the global workforce. Today we have a strong principled approach to how we design and run A/B tests on everything from UI designs to AI algorithms, and feature launches to bug fixes. As our platform continues to grow and evolve, these techniques have become even more […]

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