Talk Abstract
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.
Talk
Ruben Mak
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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2 Likes
Thanks for the interesting talk, I am curious to know more about how the theano random stream doesnt work for you.
Thanks for the question!
To be honest, it was in a different project which I can’t unfortunately share in public. Also, I tried several things and gave up at some point, so it isn’t really well documented. However, here’s a screenshot of one of my last attempts in that project:
Which gives:
In previous tries I had ‘chain x failed’ errors. Any suggestion would be welcome or if you could point me to some example implementations where something similar is done (i.e. incorporating estimation error / variance from earlier stage estimation). Also, in this case we need to sample from the beta distribution which isn’t in the default distribution in Theano random streams if I’m not mistaken. So probably I would need to do a transformation from uniform distribution. Haven’t really thought much about how to do that exactly so any suggestion would be welcome!
I have tried to put randomstream in a model ages ago, which seems to work fine: https://nbviewer.jupyter.org/github/pymc-devs/resources/blob/master/BCM/CaseStudies/MemoryRetention.ipynb
Just to confirm, have you try putting a tt.print in the model block so it prints the rng value? I guess a risk here is that the gradient is broken when a rng is added to the model
Many thanks for your reply! It definitely has to do something with specific model specification and indeed seems to be breaking some gradients. By the way, for this specific application my ‘bootstrapping approach’ shows we don’t need to worry that much about estimation error from the first stage. But sure would be interesting from a more technical perspective to look into some time!
1 Like
Hey @Rubenmak I just went through the talk and I found it incredibly helpful in my understanding of hierarchical models. Thank you for putting it together.
I did have one question about the model.
In each of the models you had something to the degree of:
p = sum(beta[i] * x_hat[:, i] for i in range(max_cap))
I’m wondering why you would sum these values? I’m trying to reason about this logic, as x_hat is is just the proportion of users who saw a given number of impressions for all the cap levels. So how would the sum of those values be helpful for the analysis? As would would we not want to just pass in the raw proportion values, not the sum of their proportions?
Thank you again for your work!
