Good fun at #measurecamptoronto talking about Privacy by design and how applying data minimization gets you both K-anonymization AND ability to efficiently construct Gramian matrices for OLS.
Off to London for @MeasureCampLDN
We have a new batch of Conductrics INC Select whiskey from @MilamandGreene over here from Texas and alfajores direct from Buenos Aires as prizes and give aways.
For talks I will do something on Privacy Engineering and AB Testing.
tl;dl Privacy by design & customer first philosophy, if not two sides of the same coin, are consistent with each other.
Collecting customer data is akin to asking for something. Customer first means asking from customers only when its a GOOD FAITH attempt to enrich THEIR lives.
"Collect everything" is easy to say. It can even be rationalized. Think a little deeper, though, and the dangers of this mindset are pretty easy to surface. That's the topic of our latest episode with @mgershoff! https://t.co/7Tx5FjgN45
I had such a great time getting to meet and chat with the Columbus analytics community. Thank you @tgwilson and the team at @cbusdaw for taking personal time to organize such a welcoming event!
I know people and I still haven't been able to get a bottle from the Wildlife collection. Gotta up my click submit game.
@MilamandGreene
https://t.co/kmAum4i3lj
Great. As an extension to her note about effects heterogeneity. I think it is more that contextual bandits/Targeting is unlikely to be valuable unless there is a theory why there should be heterogeneity. If you don't then just run a simple RCT or Epsilon-First bandit.
These recent slides from Susan Athey and Guido Imbens at NBER are a great recent review of the most valuable data science methods I'm aware of. They cover tons of ground with lots of pointers.
https://t.co/BEjcLLi2vq
Do you mind if I DM you here on X @thegautamkamath ? If not no worries. Was looking for some links/follow up around a quick question on the dual view of the Diff Privacy and Pearson-Neyman Hypth Testing.
Oh! I have to read. I have been thinking informally about this for Tech/Data Science - that methodological preferences etc. are drawn from individuals who are clustered (in part by being socially connected in the valley via a small set of interconnected VCs)
1. https://t.co/PZmrnib3Gf
Meta-analyses combine trials that are presumably *independent*. But social relatedness between researchers --> more similar effect estimates, suggesting dependency via same "school of thought"