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A Survey of Causal Inference Applications at Netflix
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
Tag : Causal Inference
6 min read
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Netflix: A Culture of Learning
Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, Colin McFarland, Mihir Tendulkar, and Travis BrooksThis is the last post in an
18 min read
Experimentation is a major focus of Data Science across Netflix
Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, Colin McFarland, Andy Rhines, Sophia Liu, Mihir Tendulkar, Kevin Mercurio, Veronica
18 min read
Experimentation is a major focus of Data Science across Netflix
Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, Colin McFarland, Andy Rhines, Sophia Liu, Mihir Tendulkar, Kevin Mercurio, Veronica
1 min read
Building confidence in a decision
Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, Michael Lindon, and Colin McFarlandThis is the fifth post in a multi-part series on
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Hunting for the Causes of Obesity, and Maybe Solutions
The chronic disease of obesity is tough to wrap our heads around. On one hand, many people consider themselves to be amateur experts. Consumer media
Causal Decision Making with non-Causal Models
In a previous post I noted: " ...correlations or 'flags' from big data might not 'identify' causal effects, but they are useful for prediction
Nutritional Epidemiology: No Longer Good Enough?
The Nutrition Source at Harvard makes one thing clear enough. Potatoes are a problem. They can give you obesity, diabetes, and heart disease. Skip
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Uplift Modeling aka Heterogeneous Treatment Effects Evaluation
This is joint work with Kenneth Foo (cross-posted from the authors’ Github article with some changes)1. Why uplift modeling?With causal inference,
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Interpreting A/B test results: false negatives and power
Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, and Colin McFarlandThis is the fourth post in a multi-part series on how Netflix
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Interpreting A/B test results: false positives and statistical significance
Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, and Colin McFarlandThis is the third post in a multi-part series on how Netflix
1 min read
What is an A/B Test?
Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, and Colin McFarlandThis is the second post in a multi-part series on how Netflix
1 min read
Decision Making at Netflix
Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, and Colin McFarlandThis introduction is the first in a multi-part series on how
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Causal Inferences and times series
In the last post, In the last post, moved by the necessity to compare time series, I browsed literature and my library of papers to find
R.A Fisher, Big Data, and Pretended Knowledge
In Thinking Fast and Slow, Kahneman points out that what matters more than the quality of evidence, is the coherence of the story. In business and