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.
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.
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.
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