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Canonical Insights

If the canonical insight of computer science is automating repetition, the canonical insight of data science is optimization. It isn’t that computer scientists haven’t thought about optimization. They have. But computer scientists weren’t the first to think about automation, just like economists weren’t the first to

Computing Optimal Cut-Offs

Probabilities from classification models can have two problems: 1. Miscalibration: A p of .9 often doesn’t mean a 90% chance of 1 (assuming a dichotomous y). (You can calibrate it using isotonic regression.) 2. Optimal cut-offs: For multi-class classifiers, we do not know what probability value will maximize the

Online Learning With Biased Sampling

Say that you train a model to predict who will click on an ad. Say that you deploy the model to only show ads to people who are likely to click on them. (For a discussion about the optimal strategy for whom to show ads to, see here.) And say

Some Facts About Indian Polling Stations

Of the 748,584 polling stations for which we have self-reported data on building conditions, nearly 24% report having Internet. A similar number report having “Landline Telephone/Fax Connection.” 97.7% report having toilets for men and women. 2.6% report being in a “dilapidated or dangerous” building. 93.2%

Growth Funnels

You spend a ton of time building a product to solve a particular problem. You launch. Then, either the kinds of people whose problem you are solving never arrive or they arrive and then leave. You want to know why because if you know the reason, you can work to

Conscious Uncoupling: Separating Compliance from Treatment

Let’s say that we want to measure the effect of a phone call encouraging people to register to vote on voting. Let’s define compliance as a person taking the call (like they do in Gerber and Green, 2000, etc.). And let’s assume that the compliance rate is

Prediction Errors: Using ML For Measurement

Say you want to measure how often people visit pornographic domains over some period. To measure that, you build a model to predict whether or not a domain hosts pornography. And let’s assume that for the chosen classification threshold, the False Positive rate (FP) is 10\% and the False

Technical Leadership: Building Great Teams and Products

Companies bet on people. They bet that the people they have hired will figure out the problems and their solutions. To make the bet credible, the golden rule is to hire competent people. Other virtues matter, but competence generally matters the most in technical roles. Once you have made the

Comparing Ad Targeting Regimes

Ad targeting is often useful when you have multiple things to sell (opportunity cost), when the cost of running an ad is non-trivial, when an irrelevant ad reduces your ability to reach the user later, or any combination of the above. (For a more formal treatment, see below.) targetingtargeting.pdf133