Causal inference (Practical AI #220)
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How to Control the Selection Process for Candles
Last year Shopify had 87,837 orders each of which included a free sample two ounce candle it's like a freebie add-on and over time the assumption has always been oh people really like that. How do you efficiently partition the population in order to reduce costs associated with an experiment? You want to be as cost efficient as possible but also still get robust conclusions out. Transfer learning is a big topic in AI so maybe let's assume you've done this experiment at this point in time and you found that robust treatment effect. The question is then in six months from now will the world have changed or will these results still be about right.
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