How Booking.com deals with Selection Bias

I came across this PyData 2018 talk by Lucas Bernadi of Booking.com where he talks about the importance of selection bias for practical applications of machine learning. We can’t just throw data into machines and expect to see any meaning […], we need to think [about this]. I see a strong trend in the practitioners…

Causal Random Forests, by Mark White

I stumbled accros this incredibly interesting read by Mark White, who discusses the (academic) theory behind, inner workings, and example (R) applications of causal random forests: EXPLICITLY OPTIMIZING ON CAUSAL EFFECTS VIA THE CAUSAL RANDOM FOREST: A PRACTICAL INTRODUCTION AND TUTORIAL (By Mark White) These so-called “honest” forests seem a great technique to identify opportunities…

Propensity Score Matching Explained Visually

Propensity score matching (wiki) is a statistical matching technique that attempts to estimate the effect of a treatment (e.g., intervention) by accounting for the factors that predict whether an individual would be eligble for receiving the treatment. The wikipedia page provides a good example setting: Say we are interested in the effects of smoking on…

How to find two identical Skittles packs?

In a hilarious experiment the anonymous mathematician behind the website Possibly Wrong estimated that s/he only needed to open “about 400-500” packs of Skittles to find an identifical pack. From January 12th up to April 6th, s/he put it to the test and counted the contents of an astonishing 468 packs, containing over 27.000 individual…

A/B Testing a New Look

This WordPress blogger I came across — let’s call him “John” for now — has a very peculiar way of testing out his looks. Using dating-apps like Tinder, John conducted A/B-tests to find out whether people would prefer him romantically with or without a beard.  Via a proper experimental setup, John found out that bearded John receives much…

12 Guidelines for Effective A/B Testing

I wrote about Emily Robinson and her A/B testing activities at Etsy before, but now she’s back with a great new blog full of practical advice: Emily provides 12 guidelines for A/B testing that help to setup effective experiments and mitigate data-driven but erroneous conclusions: Have one key metric for your experiment. Use that key…

Evolving Floorplans – by Joel Simon

Joel Simon is the genius behind an experimental project exploring optimized school blueprints. Joel used graph-contraction and ant-colony pathing algorithms as growth processes, which could generate elementary school designs optimized for all kinds of characteristics: walking time, hallway usage, outdoor views, and escape routes just to name a few.   Definitely check out the original write-up if you…