Due to unobserved confounders, users are often exposed to too many repetitive ads. We will show how we use instrumental variable analysis to prove this is ineffective for advertisers. The focus of the talk will be choosing the model assumptions and how to implement them in pymc3. Finally, we show how hierarchical modelling can be used to combine these models.
Back in 2012, Ruben introduced data science at Greenhouse, a digital advertising agency in the Netherlands. He is currently principal data scientist and cluster lead. He’s given several talks at PyData conferences and is one of the founders of PyData Eindhoven.
This is a PyMCon 2020 talk
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